Discriminative multi-layer illumination-robust feature extraction for face recognition. (July 2017)

• Record Type: Journal Article
• Authors: Yu, Yu-Feng; Dai, Dao-Qing; Ren, Chuan-Xian; Huang, Ke-Kun
• Is Part Of: Pattern recognition. Volume 67(2017:Jul.)
• Page Start: 201
• Page End: 212
• Publication Date: 2017-07
• Subjects: Face recognition -- Decomposition technique -- Illumination-invariant feature -- Discriminant filter -- Small-scale; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.02.004
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Propose multi-layer decomposition model to extract illumination invariant features. Introduce weighting mechanism to control the contribution of each layer feature. Propose a discriminative filter to remove the influence of detrimental features. An optimization algorithm is proposed to determine weight and filter simultaneously. Abstract: Tackling illumination variation is a major problem and it is also an important challenge for practical face recognition systems. Some related methods consider that lighting intensity components mainly lie in large-scale features, and they use a lot of image decomposition techniques to extract the small-scale illumination-invariant features and remove the large-scale features from original face images. However, it argues that the large-scale features contain a lot useful information which can be further extracted, and the small-scale illumination-invariant features are not robust enough due to they contain some detrimental features (noise, etc.). In this paper, we propose a discriminative multi-layer illumination-robust feature extraction (DMI) model to address this problem. First, we decompose the large-scale features into multi-layer small-scale illumination-robust features as a linear combination, and then a weight is assigned to each layer to adjust its importance and influence. The idea is to take full advantage of these useful information in large-scale features for face recognition. Second, we learn a discriminant filter to improve the robustness and statistical discriminative ability of the reconstructed illumination-robust face for face recognition under poor lighting conditions. Extensive experiments on three benchmark face databases and a video image database show that DMI performs better than the related methods, especially in difficult lighting conditions.