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Face Recognition Methods for Uncontrolled Settings

Face Recognition Methods for Uncontrolled Settings

Harry Wechsler
Copyright: © 2017 |Pages: 32
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch058
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MLA

Wechsler, Harry. "Face Recognition Methods for Uncontrolled Settings." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1440-1471. https://doi.org/10.4018/978-1-5225-1759-7.ch058

APA

Wechsler, H. (2017). Face Recognition Methods for Uncontrolled Settings. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 1440-1471). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch058

Chicago

Wechsler, Harry. "Face Recognition Methods for Uncontrolled Settings." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1440-1471. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch058

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Abstract

The overall coverage of the chapter is about moving face recognition out of the comfort zone and dramatically improving the current performance of existing biometric tools by fusing the rich spatial, temporal, and contextual information available from the multiple views made available by video (rather than still images) in the wild and operational real-world problems. Instead of relying on a “single best frame approach,” one must confront uncontrolled settings by exploiting all available imagery to allow the addition of new evidence, graceful degradation, and re-identification. Uncontrolled settings are all-encompassing and include Aging-Pose, Illumination, and Expression (A-PIE), denial and deception characteristic of incomplete and uncertain information, uncooperative users, and unconstrained data collection, scenarios, and sensors. The challenges are many: most important among them lack of persistence for biometric data, adversarial biometrics, open rather than closed set recognition, covariate shift, cross-dataset generalization, alignment and registration, interoperability, scalability, and last but not least, the deployment of full-fledged biometrics that include detection, authentication, informative sampling, and tracking. The overall recommendations are synergetic and should consider for implementation and processing purposes the regularization, statistical learning, and boosting triad complemented by sparsity and grouping (feature sharing) to deal with high-dimensional data and enhanced generalization. The recurring theme is that of a unified framework that involves multi-task and transfer learning using metric learning and side information.

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