title: A cognitively-motivated framework for partial face recognition in unconstrained scenarios.
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.
authors.affiliation: INESC TEC and Faculdade de Engenharia, Universidade do Porto, Campus da FEUP, Rua Dr. Roberto Frias, n 378, 4200-465 Porto, Portugal. jcmonteiro89@gmail.com.
authors.forename: João C
authors.initials: JC
authors.lastname: Monteiro
authors.affiliation: INESC TEC and Faculdade de Engenharia, Universidade do Porto, Campus da FEUP, Rua Dr. Roberto Frias, n 378, 4200-465 Porto, Portugal. jaime.cardoso@inescporto.pt.
authors.forename: Jaime S
authors.initials: JS
authors.lastname: Cardoso