HSR: L1/2-regularized sparse representation for fast face recognition using hierarchical feature selection. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Han, Bo; He, Bo; Sun, Tingting; Yan, Tianhong; Ma, Mengmeng; Shen, Yue; Lendasse, Amaury
• Is Part Of: Neural computing & applications. Volume 27:Issue 2(2016)
• Page Start: 305
• Page End: 320
• Publication Date: 2016-02
• Subjects: Fast face recognition -- Hierarchical feature selection -- Gabor wavelets -- ELM-AE -- Sparse representation -- L1/2 regularization -- HSR; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1907-y
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract In this paper, we propose a novel method for fast face recognition calledL 1/2 -regularized sparse representation using hierarchical feature selection. By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in. It consists of Gabor wavelets and extreme learning machine auto-encoder (ELM-AE) hierarchically. For Gabor wavelets' part, local features can be extracted at multiple scales and orientations to form Gabor-feature-based image, which in turn improves the recognition rate. Besides, in the presence of occluded face image, the scale of Gabor-feature-based global dictionary can be compressed accordingly because redundancies exist in Gabor-feature-based occlusion dictionary. For ELM-AE part, the dimension of Gabor-feature-based global dictionary can be compressed because high-dimensional face images can be rapidly represented by low-dimensional feature. By introducingL 1/2 regularization, our approach can produce sparser and more robust representation compared toL 1 -regularized sparse representation-based classification (SRC), which also contributes to the decrease of the computational cost in sparse representation. In comparison with related work such as SRC and Gabor-feature-based SRC, experimental results on a variety of face databases demonstrate the great advantage of our method for computational cost. Moreover, we also achieve approximate or even better recognition rate.