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.

Multimodal Hierarchical Face Recognition using Information from 2.5D Images

“…Schematic representation of the proposed algorithm and its main blocks: a) training of the universal background models using data from multiple individuals; b) maximum a posteriori (MAP) adaptation of the universal background models (UBM) to generate individual specific models; and (c) testing with new data from unknown sources From(Monteiro and Cardoso, 2015a) …”