Face Recognition via Compact Second-Order Image Gradient Orientations

He-Feng Yin, Xiao-Jun Wu, Cong Hu and Xiaoning Song
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He-Feng Yin: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Xiao-Jun Wu: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Cong Hu: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Xiaoning Song: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China

Mathematics, 2022, vol. 10, issue 15, 1-15

Abstract: Conventional subspace learning approaches based on image gradient orientations only employ first-order gradient information, which may ignore second-order or higher-order gradient information. Moreover, recent researches on the human vision system (HVS) have uncovered that the neural image is a landscape or a surface whose geometric properties can be captured through second-order gradient information. The second-order image gradient orientations (SOIGO) can mitigate the adverse effect of noise in face images. To reduce the redundancy of SOIGO, we propose compact SOIGO (CSOIGO) by applying linear complex principal component analysis (PCA) in SOIGO. To be more specific, the SOIGO of training data are firstly obtained. Then, linear complex PCA is applied to obtain features of reduced dimensionality. Combined with collaborative-representation-based classification (CRC) algorithm, the classification performance of CSOIGO is further enhanced. CSOIGO is evaluated under real-world disguise, synthesized occlusion, and mixed variations. Under the real disguise scenario, CSOIGO makes 2.67% and 1.09% improvement regarding accuracy when one and two neutral face images per subject are used as training samples, respectively. For the mixed variations, CSOIGO achieves a 0.86% improvement in terms of accuracy. These results indicate that the proposed method is superior to its competing approaches with few training samples, and even outperforms some prevailing deep-neural-network-based approaches.

Keywords: face recognition; second-order gradient; image gradient orientations; collaborative-representation-based classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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