Discriminant auto encoders for face recognition with expression and pose variations

Abstract

The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. This paper presents a novel non-linear discriminant error criterion which can be used in effective feature learning from raw pixels. Unlike many existing methods which assume the problem to be linear in nature, the proposed method utilizes a novel deep learning (DL) framework which makes no prior assumptions thus exploiting the full potential of learning a highly non-linear transformation. High level representations learnt via the proposed model are highly supervised and can help to boost the performance of subsequent classifiers such as LDA. This study clearly shows the value of using non-linear discriminant error criterion as a tractable objective to guide the learning of useful high level features in various face related problems. The extracted features are learnt from local face regions and the results of the experiments performed on 3 different face image databases demonstrate the superiority and the generalizability of our method compared to existing work, as well as the applicability of the concept onto many different deep learning models of the same nature.