An Asian Face Dataset and How Race Influences Face Recognition

Xiong, Zhangyang; Wang, Zhongyuan; Du, Changqing; Zhu, Rong; Xiao, Jing; Lü, Tao

doi:10.1007/978-3-030-00767-6_35

Cited by 17 publications

(12 citation statements)

References 11 publications

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“…The Caucasian dataset contains 8,005 images of 2,132 males and 2,606 images of 637 females. Subjects range in age approximately from 16 The Asian Faces Dataset (AFD) [32] was assembled using "in the wild" images scraped from the web. We curated a subset of AFD for this study by the following steps.…”

Section: Experimental Datasets and Matchermentioning

confidence: 99%

Analysis of Gender Inequality In Face Recognition Accuracy

Albiero

Krishnapriya

Vangara

et al. 2020

2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)

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We present a comprehensive analysis of how and why face recognition accuracy differs between men and women. We show that accuracy is lower for women due to the combination of (1) the impostor distribution for women having a skew toward higher similarity scores, and (2) the genuine distribution for women having a skew toward lower similarity scores. We show that this phenomenon of the impostor and genuine distributions for women shifting closer towards each other is general across datasets of African-American, Caucasian, and Asian faces. We show that the distribution of facial expressions may differ between male/female, but that the accuracy difference persists for image subsets rated confidently as neutral expression. The accuracy difference also persists for image subsets rated as close to zero pitch angle. Even when removing images with forehead partially occluded by hair/hat, the same impostor/genuine accuracy difference persists. We show that the female genuine distribution improves when only female images without facial cosmetics are used, but that the female impostor distribution also degrades at the same time. Lastly, we show that the accuracy difference persists even if a state-of-the-art deep learning method is trained from scratch using training data explicitly balanced between male and female images and subjects.

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Section: Experimental Datasets and Matchermentioning

confidence: 99%

Analysis of Gender Inequality In Face Recognition Accuracy

Albiero

Krishnapriya

Vangara

et al. 2020

2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)

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“…Similarly, modern machine learning algorithms can suffer from social biases due to a mismatch of data distribution in training and test data, e.g. deep convolutional neural networks (DCNN) trained on predominantly white faces are much worse at recognizing Asian faces (compared to White) and vice versa (Xiong et al, 2018;Tian et al, 2021). Our results on PCR and double-descent offer an interesting hypothesis why ORE happens in humans, and also for how to mitigate similar issues in machine learning algorithms.…”

Section: Other Race Effect and Semi-supervised Learningmentioning

confidence: 81%

Understanding double descent through the lens of principal component regression

2021

Preprint

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Several recent papers have studied the double descent phenomenon: a classic U-shaped empirical risk curve when the number of parameters is smaller or equal to the number of data points, followed by a decrease in empirical risk (referred to as “second descent”) as the number of features is increased past the interpolation threshold (the minimum number of parameters needed to have 0 training error). In a similar vein as several recent papers on double descent, we concentrate here on the special case of over-parameterized linear regression, one of the simplest model classes that exhibit double descent, with the aim of better understanding the nature of the solution in the second descent and how it relates to solutions in the first descent. In this paper, we show that the final second-descent model (obtained using all features) is equivalent to the model estimated using principal component (PC) regression when all PCs of training data are included. It follows that many properties of double descent can be understood through the relatively simple and well-characterized lens of PC regression. In particular, we will identify a set of conditions that will guarantee final second-descent performance to be better than the best first-descent performance: it is the scenario in which PC regression using all features does not suffer from over-fitting and can be guaranteed to outperform any other first-descent model (any linear regression model using no more features than training data points). We will also discuss how this work relates to transfer learning, semi-supervised learning, few-shot learning, as well as theoretical concepts in neuroscience.

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“…Although all available datasets contain face images of different poses, most of them are collected randomly and lack diversity in view variations [8]. Table 1 tabulates some typical multi-view face datasets.…”

Section: Public Face Datasetsmentioning

confidence: 99%

A Tilt-Angle Face Dataset And Its Validation

Wang

Zheng

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Since the surveillance cameras are usually mounted at a high position to overlook targets, tilt-angle faces on overhead view are common in the public video surveillance environment. Face recognition approaches based on deep learning models have achieved excellent performance, but there remains a large gap for the overlooking surveillance scenarios. The results of face recognition depend not only on the structure of the model, but also on the completeness and diversity of the training samples. The existing multi-pose face datasets do not cover complete top-view face samples, and the models trained by them thus cannot provide satisfactory accuracy. To this end, this paper pioneers a multi-view tilt-angle face dataset (TFD), which is collected with an elaborately devised overhead capture equipment. TFD contains 11,124 face images from 927 subjects, covering a variety of tilt angles on the overhead view. To verify the validity of the constructed dataset, we further conduct comprehensive face detection and recognition experiments using the corresponding models trained by WiderFace, Webface and our TFD, respectively. Experimental results show that our TFD substantially promotes the face detection and recognition accuracy under the top-view situation. TFD is available at https://github.com/huang1204510135/DFD.

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An Asian Face Dataset and How Race Influences Face Recognition

Cited by 17 publications

References 11 publications

Analysis of Gender Inequality In Face Recognition Accuracy

Analysis of Gender Inequality In Face Recognition Accuracy

Understanding double descent through the lens of principal component regression

A Tilt-Angle Face Dataset And Its Validation

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