Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/135179
Title: TOWARDS MULTI-MODAL FACE RECOGNITION IN THE WILD
Authors: CHAO XIONG
Keywords: Face Recognition, Face Verification, Multi-Modality, Deep Learning, Random Forest, Feature Learning
Issue Date: 30-Dec-2015
Source: CHAO XIONG (2015-12-30). TOWARDS MULTI-MODAL FACE RECOGNITION IN THE WILD. ScholarBank@NUS Repository.
Abstract: Face recognition aims at utilizing the facial appearance for the identification or verification of human individuals, and has been one of the fundamental research areas in computer vision. Over the past a few decades, face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics and so on. Many existing face recognition methods are evaluated with faces collected in labs, and does not generalize well in reality. Compared with faces captured in labs, faces in the wild are inherently multi-modal distributed. The multi-modality issue leads to significant intra-class variations, and usually requires a large amount of labeled samples to cover the wide range of modalities. These difficulties make unconstrained face recognition even more challenging, and pose a considerable gap between laboratorial research and industrial practice. To bridge the gap, we set focus on multi-modal face recognition in the unconstrained environment in this thesis.
URI: http://scholarbank.nus.edu.sg/handle/10635/135179
Appears in Collections:Ph.D Theses (Open)

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