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Face recognition technology: security versus privacy

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TLDR
The interplay of technical and social issues involved in the widespread application of video surveillance for person identification, including face recognition technology, are analyzed.
Abstract
Video surveillance and face recognition systems have become the subject of increased interest and controversy after the September 11 terrorist attacks on the United States. In favor of face recognition technology, there is the lure of a powerful tool to aid national security. On the negative side, there are fears of an Orwellian invasion of privacy. Given the ongoing nature of the controversy, and the fact that face recognition systems represent leading edge and rapidly changing technology, face recognition technology is currently a major issue in the area of social impact of technology. We analyze the interplay of technical and social issues involved in the widespread application of video surveillance for person identification.

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Citations
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Propagation of societal gender inequality by internet search algorithms

TL;DR: It is demonstrated that gender bias in a widely used internet search algorithm reflects the degree of gender inequality existing within a society, and an integrative model of ethical AI that includes human psychological processes is called for to illuminate the formation, operation, and mitigation of algorithmic bias.
Proceedings ArticleDOI

Physiological Signals: The Next Generation Authentication and Identification Methods!?

TL;DR: This article introduces a new class of biometrics that is founded on processing physiological personal features, as opposed to physical and behavioral features, which can increaseBiometrics' robustness and enables cross validation.
Proceedings ArticleDOI

Face recognition based on the combination of histogram features and rough location information of facial parts

TL;DR: An improved face recognition algorithm based on adjacent pixel intensity difference quantization (APIDQ) histogram method is proposed, which shows that top 1 recognition rate of 97.6% is achieved when evaluated by FB task of the FERET database.
Proceedings ArticleDOI

An improved fast face recognition algorithm based on adjacent pixel intensity difference quantization histogram

TL;DR: An improved face recognition algorithm based on adjacent pixel intensity difference quantization (APIDQ) histogram method proposed by Kotani et al. is presented.

Person Authentication Using Color Face Recognition

TL;DR: This work shows the performance of color face recognition with YCbCr color space using GW and LBP and shows excellent recognition rates for face images taken under severe variation in pose, illumination as well as for small resolution face images.
References
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Journal ArticleDOI

Face recognition: A literature survey

TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
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Detecting faces in images: a survey

TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
Journal ArticleDOI

Human and machine recognition of faces: a survey

TL;DR: A critical survey of existing literature on human and machine recognition of faces is presented, followed by a brief overview of the literature on face recognition in the psychophysics community and a detailed overview of move than 20 years of research done in the engineering community.
Journal ArticleDOI

Comparison and combination of ear and face images in appearance-based biometrics

TL;DR: It is found that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent in one experiment and multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric.
Journal ArticleDOI

Face Recognition in Poor-Quality Video: Evidence From Security Surveillance:

TL;DR: In this article, the authors examined the ability of subjects to identify target people captured by a commercially available video security device and found that subjects who were personally familiar with the targets performed very well at identifying them, but subjects unfamiliar with the target performed very poorly.
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