Book ChapterDOI
Fair Face Recognition Using Data Balancing, Enhancement and Fusion
Jun Yu,Xinlong Hao,Haonian Xie,Ye Yu +3 more
- pp 492-505
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TLDR
This paper proposes a fair face recognition system with low bias by reducing the influence of gender and skin colour by adding multiple preprocessing methods to improve the dual shot face detector for obtaining target face from a given test image.Abstract:
Racial bias is an important issue in biometrics, while has not been thoroughly studied in deep face recognition. By reducing the influence of gender and skin colour, this paper proposes a fair face recognition system with low bias. First, multiple preprocessing methods are added to improve the dual shot face detector for obtaining target face from a given test image. Then, a data re-sampling approach is employed to balance the data distribution and reduce the bias based on the analysis of training data. Moreover, multiple data enhancement methods are used to increase the accuracy performance. Finally, a linear-combination strategy is adopted to benefit from mutil-model fusion. ChaLearn Looking at People Fair Face Recognition challenge is supported by ECCV 2020. Our team (ustc-nelslip) ranked 1st in the development stage and 2nd in the test stage of this challenge. The code is available at https://github.com/HaoSir/ECCV-2020-Fair-Face-Recognition-challenge_2nd_place_solution-ustc-nelslip-.read more
Citations
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Book ChapterDOI
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition
TL;DR: The 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge as mentioned in this paper evaluated accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes.
Journal ArticleDOI
Software Fairness: An Analysis and Survey
TL;DR: A clear view of the state-of-the-art in software fairness analysis is provided including the need to study intersectional/sequential bias, policy-based bias handling and human-in- the-loop, socio-technical bias mitigation.
Journal ArticleDOI
Explaining Bias in Deep Face Recognition via Image Characteristics
TL;DR: The results show that trends appearing in a single-attribute analysis disappear or reverse when multi-attribute groups are considered, and that performance disparities are also related to non-protected attributes.
Journal ArticleDOI
A Survey of Fairness in Medical Image Analysis: Concepts, Algorithms, Evaluations, and Challenges
TL;DR: This paper gives a comprehensive and precise definition of fairness, followed by introducing currently used techniques in fairness issues in MedIA, and lists public medical image datasets that contain demographic attributes for facilitating the fairness research and summarize current algorithms concerning fairness in Media.
Journal ArticleDOI
The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds
TL;DR: The higher the security of the face recognition system is, the higher the disparities in usability among demographic groups are and compelling unfairness issues hence exist and urge countermeasures in real-world high-stakes environments requiring severe security levels.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: SSD as mentioned in this paper discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, and combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.
Proceedings ArticleDOI
Focal Loss for Dense Object Detection
TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.