A cognitively-motivated framework for partial face recognition in unconstrained scenarios

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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.

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Monteiro, J. C., & Cardoso, J. S. (2015). A cognitively-motivated framework for partial face recognition in unconstrained scenarios. Sensors (Switzerland), 15(1), 1903–1924. https://doi.org/10.3390/s150101903

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