-
The Influence of Width Ratios on Structural Beauty in Male Faces
Authors:
Theresa Tennstedt,
Benjamin Knopp,
Dominik Endres
Abstract:
…a data-driven foundation for understanding facial aesthetics and cognitive processes of human perception, with applications in advertising, artificial face generation, and plastic surgery.
▽ More
This study investigates the relationship between interocular distance relative to overall facial width (width ratio) and perceived subjective beauty in male faces. Building on the methodology of Pallett et al. (2010), who found that average proportions in female faces were rated as most attractive, the current study aimed to test this hypothesis in male faces. Faces from the Chicago Face Database (Ma et al., 2015) were morphed into average faces within three groups (with low, medium, and high width ratios), each composed of 96 or 97 individual images. These three average faces were then systematically manipulated in their width ratios across three levels in both directions, respectively, resulting in a total of 21 comparable faces. The use of multiple base faces served as a control for potential artifacts of image processing. Consequently, comparisons were restricted to within-group pairs to avoid confounding by co-varying facial features (e.g., skin tone), which precluded direct cross-condition comparisons but ensured internal validity. In a two-alternative forced-choice task, participants selected the more beautiful face from each pair. The data were analyzed using a Bayesian model which enables inference of the width ratio perceived as most beautiful. Results support the hypothesis that averageness in facial proportions correlates with higher perceived attractiveness. The study highlights the importance of controlling for image manipulation, including attempts at methodological implementation, and of considering ethnicity as a potential moderating variable. These findings offer a data-driven foundation for understanding facial aesthetics and cognitive processes of human perception, with applications in advertising, artificial face generation, and plastic surgery.
△ Less
Submitted 23 July, 2026; v1 submitted 13 February, 2026;
originally announced February 2026.
-
EfficientNet-Based Multi-Class Detection of Real, Deepfake, and Plastic Surgery Faces
Authors:
Li Kun,
Milena Radenkovic
Abstract:
Currently, deep learning has been utilised to tackle several difficulties in our everyday lives. It not only exhibits progress in computer vision but also constitutes the foundation for several revolutionary technologies. Nonetheless, similar to all phenomena, the use of deep learning in diverse domains has produced a multifaceted interaction of advantages and disadvantages for human society. Deep…
▽ More
Currently, deep learning has been utilised to tackle several difficulties in our everyday lives. It not only exhibits progress in computer vision but also constitutes the foundation for several revolutionary technologies. Nonetheless, similar to all phenomena, the use of deep learning in diverse domains has produced a multifaceted interaction of advantages and disadvantages for human society. Deepfake technology has advanced, significantly impacting social life. However, developments in this technology can affect privacy, the reputations of prominent personalities, and national security via software development. It can produce indistinguishable counterfeit photographs and films, potentially impairing the functionality of facial recognition systems, so presenting a significant risk.
The improper application of deepfake technology produces several detrimental effects on society. Face-swapping programs mislead users by altering persons' appearances or expressions to fulfil particular aims or to appropriate personal information. Deepfake technology permeates daily life through such techniques. Certain individuals endeavour to sabotage election campaigns or subvert prominent political figures by creating deceptive pictures to influence public perception, causing significant harm to a nation's political and economic structure.
△ Less
Submitted 12 September, 2025;
originally announced September 2025.
-
Automated Assessment of Aesthetic Outcomes in Facial Plastic Surgery
Authors:
Pegah Varghaei,
Kiran Abraham-Aggarwal,
Manoj T. Abraham,
Arun Ross
Abstract:
We introduce a scalable, interpretable computer-vision framework for quantifying aesthetic outcomes of facial plastic surgery using frontal photographs. Our pipeline leverages automated landmark detection, geometric facial symmetry computation, deep-learning-based age estimation, and nasal morphology analysis. To perfo…
▽ More
We introduce a scalable, interpretable computer-vision framework for quantifying aesthetic outcomes of facial plastic surgery using frontal photographs. Our pipeline leverages automated landmark detection, geometric facial symmetry computation, deep-learning-based age estimation, and nasal morphology analysis. To perform this study, we first assemble the largest curated dataset of paired pre- and post-operative facial images to date, encompassing 7,160 photographs from 1,259 patients. This dataset includes a dedicated rhinoplasty-only subset consisting of 732 images from 366 patients, 96.2% of whom showed improvement in at least one of the three nasal measurements with statistically significant group-level change. Among these patients, the greatest statistically significant improvements (p < 0.001) occurred in the alar width to face width ratio (77.0%), nose length to face height ratio (41.5%), and alar width to intercanthal ratio (39.3%). Among the broader frontal-view cohort, comprising 989 rigorously filtered subjects, 71.3% exhibited significant enhancements in global facial symmetry or perceived age (p < 0.01). Importantly, our analysis shows that patient identity remains consistent post-operatively, with True Match Rates of 99.5% and 99.6% at a False Match Rate of 0.01% for the rhinoplasty-specific and general patient cohorts, respectively. Additionally, we analyze inter-practitioner variability in improvement rates. By providing reproducible, quantitative benchmarks and a novel dataset, our pipeline facilitates data-driven surgical planning, patient counseling, and objective outcome evaluation across practices.
△ Less
Submitted 18 August, 2025;
originally announced August 2025.
-
Evaluation of facial landmark localization performance in a surgical setting
Authors:
Ines Frajtag,
Marko Švaco,
Filip Šuligoj
Abstract:
…is becoming increasingly widespread in various fields, including medicine. Many face detection algorithms have found applications in neurosurgery, ophthalmology, and plastic surgery. A common challenge in using these algorithms is variable lighting conditions and the flexibility of detection positions to identify and p…
▽ More
The use of robotics, computer vision, and their applications is becoming increasingly widespread in various fields, including medicine. Many face detection algorithms have found applications in neurosurgery, ophthalmology, and plastic surgery. A common challenge in using these algorithms is variable lighting conditions and the flexibility of detection positions to identify and precisely localize patients. The proposed experiment tests the MediaPipe algorithm for detecting facial landmarks in a controlled setting, using a robotic arm that automatically adjusts positions while the surgical light and the phantom remain in a fixed position. The results of this study demonstrate that the improved accuracy of facial landmark detection under surgical lighting significantly enhances the detection performance at larger yaw and pitch angles. The increase in standard deviation/dispersion occurs due to imprecise detection of selected facial landmarks. This analysis allows for a discussion on the potential integration of the MediaPipe algorithm into medical procedures.
△ Less
Submitted 24 July, 2025;
originally announced July 2025.
-
RePurr: Automated Repair of Block-Based Learners' Programs
Authors:
Sebastian Schweikl,
Gordon Fraser
Abstract:
…cover the code. At the same time, they have vastly longer runtimes due to animations and interactions on Scratch programs, which inhibits the applicability of search. (3) The plastic…
▽ More
Programming is increasingly taught using block-based languages like Scratch. While the use of blocks prevents syntax errors, learners can still make semantic mistakes, requiring feedback and help. As teachers may be overwhelmed by help requests in a classroom, may lack programming expertise themselves, or may be unavailable in independent learning scenarios, automated hint generation is desirable. Automated program repair (APR) can provide the foundation for this, but relies on multiple assumptions: (1) APR usually targets isolated bugs, but learners may fundamentally misunderstand tasks or request help for substantially incomplete code. (2) Software tests are required to guide the search and localize broken blocks, but tests for block-based programs are different to those in past APR research: They consist of system tests, and very few of them already fully cover the code. At the same time, they have vastly longer runtimes due to animations and interactions on Scratch programs, which inhibits the applicability of search. (3) The plastic surgery hypothesis assumes the code necessary for repairs already exists in the codebase. Block-based programs tend to be small and may lack this redundancy. To study if APR of such programs is still feasible, we introduce, to the best of our knowledge, the first APR approach for Scratch based on evolutionary search. Our RePurr prototype includes novel refinements of fault localization to improve the guidance of test suites, recovers the plastic surgery hypothesis by exploiting that learning scenarios provide model and student solutions, and reduces the costs of fitness evaluations via test parallelization and acceleration. Empirical evaluation on a set of real learners' programs confirms the anticipated challenges, but also demonstrates APR can still effectively improve and fix learners' programs, enabling automated generation of hints and feedback.
△ Less
Submitted 16 April, 2025;
originally announced April 2025.
-
Improving Retrieval-Augmented Deep Assertion Generation via Joint Training
Authors:
Quanjun Zhang,
Chunrong Fang,
Yi Zheng,
Ruixiang Qian,
Shengcheng Yu,
Yuan Zhao,
Jianyi Zhou,
Yun Yang,
Tao Zheng,
Zhenyu Chen
Abstract:
…automated assertion generation approach that leverages external codebases and joint training to address various technical limitations of prior work. Inspired by the plastic surgery hypothesis, AG-RAG attempts to combine relevant unit tests and advanced pre-trained language models (PLMs) with retrieval-augmented fine-tu…
▽ More
Unit testing attempts to validate the correctness of basic units of the software system under test and has a crucial role in software development and testing. Very recent work proposes a retrieve-and-edit approach to generate unit test oracles, i.e., assertions. Despite being promising, it is still far from perfect due to some limitations, such as splitting assertion retrieval and generation into two separate components without benefiting each other. In this paper, we propose AG-RAG, a retrieval-augmented automated assertion generation approach that leverages external codebases and joint training to address various technical limitations of prior work. Inspired by the plastic surgery hypothesis, AG-RAG attempts to combine relevant unit tests and advanced pre-trained language models (PLMs) with retrieval-augmented fine-tuning. AG-RAG builds a dense retriever to search for relevant test-assert pairs (TAPs) with semantic matching and a retrieval-augmented generator to synthesize accurate assertions with the focal-test and retrieved TAPs as input. Besides, AG-RAG leverages a code-aware language model CodeT5 as the cornerstone to facilitate both assertion retrieval and generation tasks. Furthermore, the retriever is optimized in conjunction with the generator as a whole pipeline with a joint training strategy. This unified design fully adapts both components specifically for retrieving more useful TAPs, thereby generating accurate assertions. We extensively evaluate AG-RAG against six state-of-the-art AG approaches on two benchmarks and three metrics. Experimental results show that AG-RAG significantly outperforms previous AG approaches on all benchmarks and metrics, e.g., improving the most recent baseline EditAS by 20.82% and 26.98% in terms of accuracy. AG-RAG also correctly generates 1739 and 2866 unique assertions that all baselines fail to generate, 3.45X and 9.20X more than EditAS.
△ Less
Submitted 24 February, 2025; v1 submitted 15 February, 2025;
originally announced February 2025.
-
Iterative Refinement Strategy for Automated Data Labeling: Facial Landmark Diagnosis in Medical Imaging
Authors:
Yu-Hsi Chen
Abstract:
…strategies for automated data labeling in facial landmark diagnosis to enhance accuracy and efficiency for deep learning models in medical applications, including dermatology, plastic surgery, and ophthalmology. Leveraging feedback mechanisms and advanced algorithms, our approach iteratively refines initial labels, red…
▽ More
Automated data labeling techniques are crucial for accelerating the development of deep learning models, particularly in complex medical imaging applications. However, ensuring accuracy and efficiency remains challenging. This paper presents iterative refinement strategies for automated data labeling in facial landmark diagnosis to enhance accuracy and efficiency for deep learning models in medical applications, including dermatology, plastic surgery, and ophthalmology. Leveraging feedback mechanisms and advanced algorithms, our approach iteratively refines initial labels, reducing reliance on manual intervention while improving label quality. Through empirical evaluation and case studies, we demonstrate the effectiveness of our proposed strategies in deep learning tasks across medical imaging domains. Our results highlight the importance of iterative refinement in automated data labeling to enhance the capabilities of deep learning systems in medical imaging applications.
△ Less
Submitted 8 April, 2024;
originally announced April 2024.
-
Face-GPS: A Comprehensive Technique for Quantifying Facial Muscle Dynamics in Videos
Authors:
Juni Kim,
Zhikang Dong,
Pawel Polak
Abstract:
…such as those captured on personal smartphones. Our approach emphasizes practicality and accessibility. It has significant potential for applications in national security and plastic surgery. Additionally, it offers remote diagnosis and monitoring for medical conditions such as stroke, Bell's palsy, and acoustic n…
▽ More
We introduce a novel method that combines differential geometry, kernels smoothing, and spectral analysis to quantify facial muscle activity from widely accessible video recordings, such as those captured on personal smartphones. Our approach emphasizes practicality and accessibility. It has significant potential for applications in national security and plastic surgery. Additionally, it offers remote diagnosis and monitoring for medical conditions such as stroke, Bell's palsy, and acoustic neuroma. Moreover, it is adept at detecting and classifying emotions, from the overt to the subtle. The proposed face muscle analysis technique is an explainable alternative to deep learning methods and a non-invasive substitute to facial electromyography (fEMG).
△ Less
Submitted 10 January, 2024;
originally announced January 2024.
-
3D Face Reconstruction: the Road to Forensics
Authors:
Simone Maurizio La Cava,
Giulia Orrù,
Martin Drahansky,
Gian Luca Marcialis,
Fabio Roli
Abstract:
3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bring…
▽ More
3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them.
△ Less
Submitted 20 September, 2023;
originally announced September 2023.
-
Periocular biometrics: databases, algorithms and directions
Authors:
Fernando Alonso-Fernandez,
Josef Bigun
Abstract:
…from the periocular region have been also used successfully for gender classification and ethnicity classification, and to study the impact of gender transformation or plastic surgery in the recognition performance. This paper presents a review of the state of the art in periocular biometric research, providing an insi…
▽ More
Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions. Periocular refers to the facial region in the eye vicinity, including eyelids, lashes and eyebrows. It is available over a wide range of acquisition distances, representing a trade-off between the whole face (which can be occluded at close distances) and the iris texture (which do not have enough resolution at long distances). Since the periocular region appears in face or iris images, it can be used also in conjunction with these modalities. Features extracted from the periocular region have been also used successfully for gender classification and ethnicity classification, and to study the impact of gender transformation or plastic surgery in the recognition performance. This paper presents a review of the state of the art in periocular biometric research, providing an insight of the most relevant issues and giving a thorough coverage of the existing literature. Future research trends are also briefly discussed.
△ Less
Submitted 26 July, 2023;
originally announced July 2023.
-
Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts
Authors:
Alexandrea K. Ramnarine
Abstract:
…models, this study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery based on a tweet or subreddit post with almost 90% accuracy. Furthermore, the model is capable of achieving higher accuracy on the un…
▽ More
The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases based on the sheer volume and velocity of textual data. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. Using a word ranking method, term frequency-inverse document frequency (TF-IDF), to create features across documents, it is possible to perform unsupervised analytics, machine learning (ML) that can group the documents without a human manually labeling the data. For large datasets with thousands of features, t-distributed stochastic neighbor embedding (t-SNE), k-means clustering and Latent Dirichlet allocation (LDA) are employed to learn top words and generate topics for a Reddit and Twitter combined corpus. Using extremely simple deep learning models, this study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery based on a tweet or subreddit post with almost 90% accuracy. Furthermore, the model is capable of achieving higher accuracy on the unsupervised sentiment task than on a rudimentary supervised document classification task. Therefore, unsupervised learning may be considered a viable option in labeling social media documents for NLP tasks.
△ Less
Submitted 5 July, 2023;
originally announced July 2023.
-
GPT-4 to GPT-3.5: 'Hold My Scalpel' -- A Look at the Competency of OpenAI's GPT on the Plastic Surgery In-Service Training Exam
Authors:
Jonathan D. Freedman,
Ian A. Nappier
Abstract:
The Plastic Surgery In-Service Training Exam (PSITE) is an important indicator of resident proficiency and serves as a useful benchmark for evaluating OpenAI's GPT. Unlike many of the simulated tests or practice questions shown in the GPT-4 Technical Paper, the multiple-choice questions evaluated here are authentic…
▽ More
The Plastic Surgery In-Service Training Exam (PSITE) is an important indicator of resident proficiency and serves as a useful benchmark for evaluating OpenAI's GPT. Unlike many of the simulated tests or practice questions shown in the GPT-4 Technical Paper, the multiple-choice questions evaluated here are authentic PSITE questions. These questions offer realistic clinical vignettes that a plastic surgeon commonly encounters in practice and scores highly correlate with passing the written boards required to become a Board Certified Plastic Surgeon. Our evaluation shows dramatic improvement of GPT-4 (without vision) over GPT-3.5 with both the 2022 and 2021 exams respectively increasing the score from 8th to 88th percentile and 3rd to 99th percentile. The final results of the 2023 PSITE are set to be released on April 11, 2023, and this is an exciting moment to continue our research with a fresh exam. Our evaluation pipeline is ready for the moment that the exam is released so long as we have access via OpenAI to the GPT-4 API. With multimodal input, we may achieve superhuman performance on the 2023.
△ Less
Submitted 3 April, 2023;
originally announced April 2023.
-
Deep Selection: A Fully Supervised Camera Selection Network for Surgery Recordings
Authors:
Ryo Hachiuma,
Tomohiro Shimizu,
Hideo Saito,
Hiroki Kajita,
Yoshifumi Takatsume
Abstract:
…the camera selection probability from multiple video sequences by learning the supervision of the expert annotation. We created a dataset in which six different types of plastic surgery are recorded, and we provided the annotation of camera switching. Our experiments show that our approach successfully switched between…
▽ More
Recording surgery in operating rooms is an essential task for education and evaluation of medical treatment. However, recording the desired targets, such as the surgery field, surgical tools, or doctor's hands, is difficult because the targets are heavily occluded during surgery. We use a recording system in which multiple cameras are embedded in the surgical lamp, and we assume that at least one camera is recording the target without occlusion at any given time. As the embedded cameras obtain multiple video sequences, we address the task of selecting the camera with the best view of the surgery. Unlike the conventional method, which selects the camera based on the area size of the surgery field, we propose a deep neural network that predicts the camera selection probability from multiple video sequences by learning the supervision of the expert annotation. We created a dataset in which six different types of plastic surgery are recorded, and we provided the annotation of camera switching. Our experiments show that our approach successfully switched between cameras and outperformed three baseline methods.
△ Less
Submitted 28 March, 2023;
originally announced March 2023.
-
3D Face Reconstruction for Forensic Recognition -- A Survey
Authors:
Simone Maurizio La Cava,
Giulia Orrù,
Tomáš Goldmann,
Martin Drahansky,
Gian Luca Marcialis
Abstract:
3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role…
▽ More
3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role in bringing evidence to a lawsuit. Shedding some light on this matter is the goal of the present survey, where we start by clarifying the relation between forensic applications and biometrics. To our knowledge, no previous work adopted this relation to make the point on the state of the art. Therefore, we analyzed the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discussed the current obstacles that separate 3D face reconstruction from an active role in forensic applications.
△ Less
Submitted 3 February, 2023;
originally announced March 2023.
-
Revisiting the Plastic Surgery Hypothesis via Large Language Models
Authors:
Chunqiu Steven Xia,
Yifeng Ding,
Lingming Zhang
Abstract:
…performance on many repair datasets, the LLMs used for direct repair are not fully aware of the project-specific information such as unique variable or method names.
The plastic…
▽ More
Automated Program Repair (APR) aspires to automatically generate patches for an input buggy program. Traditional APR tools typically focus on specific bug types and fixes through the use of templates, heuristics, and formal specifications. However, these techniques are limited in terms of the bug types and patch variety they can produce. As such, researchers have designed various learning-based APR tools with recent work focused on directly using Large Language Models (LLMs) for APR. While LLM-based APR tools are able to achieve state-of-the-art performance on many repair datasets, the LLMs used for direct repair are not fully aware of the project-specific information such as unique variable or method names.
The plastic surgery hypothesis is a well-known insight for APR, which states that the code ingredients to fix the bug usually already exist within the same project. Traditional APR tools have largely leveraged the plastic surgery hypothesis by designing manual or heuristic-based approaches to exploit such existing code ingredients. However, as recent APR research starts focusing on LLM-based approaches, the plastic surgery hypothesis has been largely ignored. In this paper, we ask the following question: How useful is the plastic surgery hypothesis in the era of LLMs? Interestingly, LLM-based APR presents a unique opportunity to fully automate the plastic surgery hypothesis via fine-tuning and prompting. To this end, we propose FitRepair, which combines the direct usage of LLMs with two domain-specific fine-tuning strategies and one prompting strategy for more powerful APR. Our experiments on the widely studied Defects4j 1.2 and 2.0 datasets show that FitRepair fixes 89 and 44 bugs (substantially outperforming the best-performing baseline by 15 and 8), respectively, demonstrating a promising future of the plastic surgery hypothesis in the era of LLMs.
△ Less
Submitted 9 December, 2024; v1 submitted 18 March, 2023;
originally announced March 2023.
-
W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data
Authors:
Gautam Rajendrakumar Gare,
Jiayuan Li,
Rohan Joshi,
Mrunal Prashant Vaze,
Rishikesh Magar,
Michael Yousefpour,
Ricardo Luis Rodriguez,
John Micheal Galeotti
Abstract:
…as our initial clinical goal since it has diverse intermixed tissues, is challenging to segment, and is an underrepresented research area. SubQ potential applications include plastic surgery, adipose stem-cell harvesting, lymphatic monitoring, and possibly detection/treatment of certain types of tumors. A custom datase…
▽ More
We present W-Net, a novel Convolution Neural Network (CNN) framework that employs raw ultrasound waveforms from each A-scan, typically referred to as ultrasound Radio Frequency (RF) data, in addition to the gray ultrasound image to semantically segment and label tissues. Unlike prior work, we seek to label every pixel in the image, without the use of a background class. To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyses ultrasound raw RF data along with the gray image. International patent(s) pending [PCT/US20/37519]. We chose subcutaneous tissue (SubQ) segmentation as our initial clinical goal since it has diverse intermixed tissues, is challenging to segment, and is an underrepresented research area. SubQ potential applications include plastic surgery, adipose stem-cell harvesting, lymphatic monitoring, and possibly detection/treatment of certain types of tumors. A custom dataset consisting of hand-labeled images by an expert clinician and trainees are used for the experimentation, currently labeled into the following categories: skin, fat, fat fascia/stroma, muscle and muscle fascia. We compared our results with U-Net and Attention U-Net. Our novel \emph{W-Net}'s RF-Waveform input and architecture increased mIoU accuracy (averaged across all tissue classes) by 4.5\% and 4.9\% compared to regular U-Net and Attention U-Net, respectively. We present analysis as to why the Muscle fascia and Fat fascia/stroma are the most difficult tissues to label. Muscle fascia in particular, the most difficult anatomic class to recognize for both humans and AI algorithms, saw mIoU improvements of 13\% and 16\% from our W-Net vs U-Net and Attention U-Net respectively.
△ Less
Submitted 2 September, 2020; v1 submitted 27 August, 2020;
originally announced August 2020.
-
Is 40 the new 60? How popular media portrays the employability of older software developers
Authors:
Sebastian Baltes,
George Park,
Alexander Serebrenik
Abstract:
…(perceived) employability issues and potential mitigation strategies. Besides rather controversial strategies such as disguising age-related aspects in résumés or undergoing plastic surgeries to appear young, we highlight the importance of keeping up-to-date, specializing in certain tasks or technologies, and present r…
▽ More
Alerted by our previous research as well as media reports and discussions in online forums about ageism in the software industry, we set out to study the public discourse around age and software development. With a focus on the USA, we analyzed popular online articles and related discussions on Hacker News through the lens of (perceived) employability issues and potential mitigation strategies. Besides rather controversial strategies such as disguising age-related aspects in résumés or undergoing plastic surgeries to appear young, we highlight the importance of keeping up-to-date, specializing in certain tasks or technologies, and present role transitions as a way forward for veteran developers. With this article, we want to build awareness among decision makers in software projects to help them anticipate and mitigate challenges that their older employees may face.
△ Less
Submitted 26 June, 2020; v1 submitted 13 April, 2020;
originally announced April 2020.
-
Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches
Authors:
Shuai Yang,
Zhangyang Wang,
Jiaying Liu,
Zongming Guo
Abstract:
…often demonstrate huge variety among different users, demanding even higher generalizability and robustness for the editing model to work. In this paper, we propose Deep Plastic Surgery, a novel, robust and controllable image editing framework that allows users to interactively edit images using hand-drawn sketch input…
▽ More
Sketch-based image editing aims to synthesize and modify photos based on the structural information provided by the human-drawn sketches. Since sketches are difficult to collect, previous methods mainly use edge maps instead of sketches to train models (referred to as edge-based models). However, sketches display great structural discrepancy with edge maps, thus failing edge-based models. Moreover, sketches often demonstrate huge variety among different users, demanding even higher generalizability and robustness for the editing model to work. In this paper, we propose Deep Plastic Surgery, a novel, robust and controllable image editing framework that allows users to interactively edit images using hand-drawn sketch inputs. We present a sketch refinement strategy, as inspired by the coarse-to-fine drawing process of the artists, which we show can help our model well adapt to casual and varied sketches without the need for real sketch training data. Our model further provides a refinement level control parameter that enables users to flexibly define how "reliable" the input sketch should be considered for the final output, balancing between sketch faithfulness and output verisimilitude (as the two goals might contradict if the input sketch is drawn poorly). To achieve the multi-level refinement, we introduce a style-based module for level conditioning, which allows adaptive feature representations for different levels in a singe network. Extensive experimental results demonstrate the superiority of our approach in improving the visual quality and user controllablity of image editing over the state-of-the-art methods.
△ Less
Submitted 9 January, 2020;
originally announced January 2020.
-
Face Identification using Local Ternary Tree Pattern based Spatial Structural Components
Authors:
Rinku Datta Rakshit,
Dakshina Ranjan Kisku,
Massimo Tistarelli,
Phalguni Gupta
Abstract:
…local descriptor from a face image plays a crucial role in face identification task in the presence of a variety of face images including constrained, unconstrained and plastic…
▽ More
This paper reports a face identification system which makes use of a novel local descriptor called Local Ternary Tree Pattern (LTTP). Exploiting and extracting distinctive local descriptor from a face image plays a crucial role in face identification task in the presence of a variety of face images including constrained, unconstrained and plastic surgery images. LTTP has been used to extract robust and useful spatial features which use to describe the various structural components on a face. To extract the features, a ternary tree is formed for each pixel with its eight neighbors in each block. LTTP pattern can be generated in four forms such as LTTP Left Depth (LTTP LD), LTTP Left Breadth (LTTP LB), LTTP Right Depth (LTTP RD) and LTTP Right Breadth (LTTP RB). The encoding schemes of these patterns are very simple and efficient in terms of computational as well as time complexity. The proposed face identification system is tested on six face databases, namely, the UMIST, the JAFFE, the extended Yale face B, the Plastic Surgery, the LFW and the UFI. The experimental evaluation demonstrates the most promising results considering a variety of faces captured under different environments. The proposed LTTP based system is also compared with some local descriptors under identical conditions.
△ Less
Submitted 16 July, 2020; v1 submitted 2 May, 2019;
originally announced May 2019.
-
On Matching Faces with Alterations due to Plastic Surgery and Disguise
Authors:
Saksham Suri,
Anush Sankaran,
Mayank Vatsa,
Richa Singh
Abstract:
Plastic…
▽ More
Plastic surgery and disguise variations are two of the most challenging co-variates of face recognition. The state-of-art deep learning models are not sufficiently successful due to the availability of limited training samples. In this paper, a novel framework is proposed which transfers fundamental visual features learnt from a generic image dataset to supplement a supervised face recognition model. The proposed algorithm combines off-the-shelf supervised classifier and a generic, task independent network which encodes information related to basic visual cues such as color, shape, and texture. Experiments are performed on IIITD plastic surgery face dataset and Disguised Faces in the Wild (DFW) dataset. Results showcase that the proposed algorithm achieves state of the art results on both the datasets. Specifically on the DFW database, the proposed algorithm yields over 87% verification accuracy at 1% false accept rate which is 53.8% better than baseline results computed using VGGFace.
△ Less
Submitted 18 November, 2018;
originally announced November 2018.
-
A Survey on Periocular Biometrics Research
Authors:
Fernando Alonso-Fernandez,
Josef Bigun
Abstract:
…of the periocular area, v) comparison with iris and face modalities, vi) soft-biometrics (gender/ethnicity classification), and vii) impact of gender transformation and plastic surgery on the recognition accuracy. This work is expected to provide an insight of the most relevant issues in periocular biometrics, giving a…
▽ More
Periocular refers to the facial region in the vicinity of the eye, including eyelids, lashes and eyebrows. While face and irises have been extensively studied, the periocular region has emerged as a promising trait for unconstrained biometrics, following demands for increased robustness of face or iris systems. With a surprisingly high discrimination ability, this region can be easily obtained with existing setups for face and iris, and the requirement of user cooperation can be relaxed, thus facilitating the interaction with biometric systems. It is also available over a wide range of distances even when the iris texture cannot be reliably obtained (low resolution) or under partial face occlusion (close distances). Here, we review the state of the art in periocular biometrics research. A number of aspects are described, including: i) existing databases, ii) algorithms for periocular detection and/or segmentation, iii) features employed for recognition, iv) identification of the most discriminative regions of the periocular area, v) comparison with iris and face modalities, vi) soft-biometrics (gender/ethnicity classification), and vii) impact of gender transformation and plastic surgery on the recognition accuracy. This work is expected to provide an insight of the most relevant issues in periocular biometrics, giving a comprehensive coverage of the existing literature and current state of the art.
△ Less
Submitted 8 October, 2018;
originally announced October 2018.
-
Eigenspace-Based Minimum Variance Adaptive Beamformer Combined with Delay Multiply and Sum: Experimental Study
Authors:
Moein Mozaffarzadeh,
Ali Mahloojifar,
Mohammadreza Nasiriavanaki,
Mahdi Orooji
Abstract:
Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based…
▽ More
Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformer has been combined with DMAS algebra, called EIBMV-DMAS, using the expansion of DMAS algorithm. The proposed method is used as the reconstruction algorithm in linear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitative and qualitative results show that it outperforms DAS, DMAS and EIBMV. The proposed method degrades the sidelobes for about 365 %, 221 % and 40 %, compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improves the SNR about 158 %, 63 % and 20 %, respectively.
△ Less
Submitted 4 October, 2017;
originally announced October 2017.
-
Expressing Facial Structure and Appearance Information in Frequency Domain for Face Recognition
Authors:
Chollette C. Olisah,
Solomon Nunoo,
Peter Ofedebe,
Ghazali Sulong
Abstract:
…the best fit model for reporting a more effective representation of the LEGGM patterns. The proposed descriptor-based face recognition method is evaluated on three databases: Plastic…
▽ More
Beneath the uncertain primitive visual features of face images are the primitive intrinsic structural patterns (PISP) essential for characterizing a sample face discriminative attributes. It is on this basis that this paper presents a simple yet effective facial descriptor formed from derivatives of Gaussian and Gabor Wavelets. The new descriptor is coined local edge gradient Gabor magnitude (LEGGM) pattern. LEGGM first uncovers the PISP locked in every pixel through determining the pixel gradient in relation to its neighbors using the Derivatives of Gaussians. Then, the resulting output is embedded into the global appearance of the face which are further processed using Gabor wavelets in order to express its frequency characteristics. Additionally, we adopted various subspace models for dimensionality reduction in order to ascertain the best fit model for reporting a more effective representation of the LEGGM patterns. The proposed descriptor-based face recognition method is evaluated on three databases: Plastic surgery, LFW, and GT face databases. Through experiments, using a base classifier, the efficacy of the proposed method is demonstrated, especially in the case of plastic surgery database. The heterogeneous database, which we created to typify real-world scenario, show that the proposed method is to an extent insensitive to image formation factors with impressive recognition performances.
△ Less
Submitted 28 April, 2017;
originally announced April 2017.
-
Vector variational problem with knitting boundary conditions
Authors:
G. Carita,
V. Goncharov,
G. Smirnov
Abstract:
…variational problem with a polyconvex integrand and nonstandard boundary conditions that can be treated as minimization of the stress energy during the suturing process in the plastic surgery. Ex- istence of minimizers is proved as well as necessary optimality conditions are discussed.
▽ More
We consider a variational problem with a polyconvex integrand and nonstandard boundary conditions that can be treated as minimization of the stress energy during the suturing process in the plastic surgery. Ex- istence of minimizers is proved as well as necessary optimality conditions are discussed.
△ Less
Submitted 10 July, 2015;
originally announced July 2015.
