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Sensors 2015, 15(1), 1903-1924; doi:10.3390/s150101903

A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios

INESC TEC and Faculdade de Engenharia, Universidade do Porto, Campus da FEUP, Rua Dr. Roberto Frias, n 378, 4200-465 Porto, Portugal
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Received: 24 November 2014 / Accepted: 7 January 2015 / Published: 16 January 2015
(This article belongs to the Section Physical Sensors)
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Abstract

Humans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is severely compromised in non-ideal image acquisition scenarios. In an attempt to deal with conditions, such as occlusion and heterogeneous illumination, we propose a new approach motivated by the global precedent hypothesis of the human brain’s cognitive mechanisms of perception. An automatic modeling of SIFT keypoint descriptors using a Gaussian mixture model (GMM)-based universal background model method is proposed. A decision is, then, made in an innovative hierarchical sense, with holistic information gaining precedence over a more detailed local analysis. The algorithm was tested on the ORL, ARand Extended Yale B Face databases and presented state-of-the-art performance for a variety of experimental setups. View Full-Text
Keywords: biometrics; face recognition; partial data; universal background model; Gaussian mixture models biometrics; face recognition; partial data; universal background model; Gaussian mixture models
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Monteiro, J.C.; Cardoso, J.S. A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios. Sensors 2015, 15, 1903-1924.

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