Joint feature extraction and classification in a unified framework for cost-sensitive face recognition

Abstract

Cost-sensitive face recognition is a challenging problem in pattern recognition. Due to the high-dimensional face features, cost-sensitive face recognition usually conducts feature extraction in advance, followed by the learning of classifier in reduced subspace. However, the pre-extracted face features are kept fixed and may suboptimal for subsequent classifier learning, which will degrade the final face recognition performance. Besides, most of face learners are cost insensitive. Even the cost-sensitive methods proposed for face recognition, they only incorporate the cost information in feature extraction or classification phase as an alternative. There is no doubt that some cost-sensitive information will be lost in their cost insensitive steps. To deal with these issues, this paper proposes to incorporate feature extraction and classification in a unified cost-sensitive framework for face recognition. The experimental results on three public face benchmarks, including Extended Yale B, CMU PIE and LFW datasets, demonstrate that the proposed method can significantly reduce the overall misclassification loss of face recognition system as well as the classification errors associated with high costs, when comparing with eleven state-of-the-art face learners and nine cost-sensitive methods.