%0 Journal Article
%T A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios
%A Joo C. Monteiro
%A Jaime S. Cardoso
%J Sensors
%P 1903-1924
%D 2015
%I MDPI AG
%R 10.3390/s150101903
%X 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.
%K biometrics
%K face recognition
%K partial data
%K universal background model
%K Gaussian mixture models
%U http://www.mdpi.com/1424-8220/15/1/1903