A meta-analysis of face recognition covariates

Lui, Yui Man; Bolme, David S.; Draper, Bruce A.; Beveridge, J. Ross; Givens, Geoff; Phillips, P. Jonathon

doi:10.1109/btas.2009.5339025

Cited by 83 publications

(69 citation statements)

References 23 publications

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“…Both of these findings are consistent with previous face recognition covariate studies [3], [19]. While it is important to measure gender and race influence, it is also important to notice they are secondary factors in terms of importance relative to environment, sensor or subject variation.…”

Section: Covariate Analysissupporting

confidence: 80%

Report on the FG 2015 Video Person Recognition Evaluation

Beveridge

Zhang

Draper

et al. 2015

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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Section: Covariate Analysissupporting

confidence: 80%

Report on the FG 2015 Video Person Recognition Evaluation

Beveridge

Zhang

Draper

et al. 2015

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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“…Some of these have already been cited in the research literature. For instance, Lui et al [8] mentioned in their work that face recognition system is sensitive to the variations in head pose, illumination, age, facial expression, aging template and resolution of acquired images. It is also well known that fingerprint is sensitive to differences in temperature, humidity and the pressure of the finger; speech is sensitive to changes in audible noise, and etc.…”

Section: Interactive Quality-driven Feedback For Biometric Systemsmentioning

confidence: 99%

Interactive quality-driven feedback for biometric systems

Wong

Poh

Kittler

et al. 2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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The application of biometric technology has so far been top-down, driven by governments and law enforcement agencies. The low demand of this technology from the public, despite its many advantages compared to the traditional means of authentication is probably due to the lack of human factor considerations in the design process. In this work, we propose a guideline to design an interactive quality-driven feedback mechanism. The mechanism aims to improve the quality of biométrie samples during the acquisition process by putting in place objective assessment of the quality and feeding this information back to the user instantaneously, thus eliminating subjective quality judgement by the user. We illustrate the feasibility of the design methodology using face recognition as a case study. Preliminary results show that the methodology can potentially increase efficiency, effectiveness and accessibility of a biométrie system

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“…Empirical studies [4] showed that minimum face image resolution between 32x32 and 64x64 is required for existing algorithms. However, a wide range of applications cannot provide enough resolution of face for recognition, such as small-scale stand-alone camera applications in banks and supermarkets, large-scale multiple networked close-circuit television (CCTV) in law enforcement applications in public streets , etc.…”

Section: A Background and Motivationmentioning

confidence: 99%

Very low resolution face recognition problem

Wilman

Yuen

2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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This paper addresses the very low resolution (VLR) problem in face recognition in which the resolution of face image to be recognized is lower than 16x16. The VLR problem happens in many surveillance camera-based applications and existing face recognition algorithms are not able to give satisfactory performance on VLR face image. While face super-resolution (SR) methods can be employed to enhance the resolution of the images, the existing learning-based face SR methods do not perform well on such a very low resolution face image. To overcome this problem, this paper models the SR problem under VLR case as a regression problem with two constraints. First, a new data constraint is design to perform the error measurement on high resolution image space which provides more detailed and discriminative information. Second, discriminative constraint is proposed and incorporated in the training stage so that the reconstructed HR image has higher discriminability. CMU-PIE, FRGC and surveillant camera face (SCface) databases are selected for experiments. Experimental results show that the proposed method outperforms the existing methods, in terms of image quality and recognition accuracy.

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A meta-analysis of face recognition covariates

Cited by 83 publications

References 23 publications

Report on the FG 2015 Video Person Recognition Evaluation

Report on the FG 2015 Video Person Recognition Evaluation

Interactive quality-driven feedback for biometric systems

Very low resolution face recognition problem

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