Spectrum-aware discriminative deep feature learning for multi-spectral face recognition. (March 2021)

• Record Type: Journal Article
• Authors: Wu, Fei; Jing, Xiao-Yuan; Feng, Yujian; Ji, Yi-mu; Wang, Ruchuan
• Is Part Of: Pattern recognition. Volume 111(2021)
• Publication Date: 2021-03
• Subjects: Deep feature learning -- Inter-spectrum correlation -- Intra- and inter-spectrum discrimination -- Multi-spectral face recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107632
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Deep metric learning technique is first introduced into the multi-spectral face recognition task. The spectrum-aware embedding loss takes both the spectrum and class label information into consideration. The multi-spectral discriminant correlation loss fully exploits the useful correlation information in multi-spectral images. The proposed approach significantly outperforms state-of-the-art multi-spectral face recognition methods. Abstract: One primary challenge of face recognition is that the performance is seriously affected by varying illumination. Multi-spectral imaging can capture face images in the visible spectrum and beyond, which is deemed to be an effective technology in response to this challenge. For current multi-spectral imaging-based face recognition methods, how to fully explore the discriminant and correlation features from both the intra-spectrum and inter-spectrum aspects with only a limited number of multi-spectral samples for model training has not been well studied. To address this problem, in this paper, we propose a novel face recognition approach named Spectrum-aware Discriminative Deep Learning (SDDL). To take full advantage of the multi-spectral training samples, we build a discriminative multi-spectral network (DMN) and take face sample pairs as the input of the network. By jointly considering the spectrum and the class label information, SDDL trains the network for projecting samples pairs into a discriminant feature subspace, on which the intrinsic relationship including the intra- and inter-spectrum discrimination and the inter-spectrum correlation among face samples is well discovered. The proposed approach is evaluated on three widely used datasets HK PolyU, CMU, and UWA. Extensive experimental results demonstrate the superiority of SDDL over state-of-the-art competing methods.