caa a22 4500 606159940 CHVBK 20210128100627.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1863-6 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1863-6 Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition [Elektronische Daten] [Zi Liu, Xiaoning Song, Zhenmin Tang] The horizontal axis-symmetrical nature of faces is useful and interesting, which has successfully applied in face detection. In general, images of faces are not strictly captured under a frontal and natural pose. It implies that extra training samples could be generated by means of the symmetry of the face. In this paper, we develop a framework that fuses virtual mirror synthesized training samples as bases and hierarchical multi-scale local binary patterns (LBP) features for classification. More specifically, in the first stage of proposed method, sampling uncertainty of the linear approximation model is alleviated effectively by constructing extra synthesized mirror training samples which generated from original images. Subsequently, in the second stage, features are extracted from the above dictionary using a hierarchical multi-scale LBP scheme. In the third stage, we combine the synthesized samples and original ones together to describe a test sample and simultaneously determine the relatively informative training samples by means of exploiting the reconstruction deviation of all the dictionary atoms. Besides, the introduced sparse coding process with weak l 1 constraint has superior competitiveness that the accuracy has been improved, while the complexity has been fallen. Ultimately, the following step determines again a new decomposition coefficient of remaining samples, which makes final decision of the classification. Experimental results that are engaged in various benchmark face databases have demonstrated the effectiveness of our algorithm. The Natural Computing Applications Forum, 2015 Sparse representation nationallicence Face recognition nationallicence Sparse residuals measurement nationallicence Image classification nationallicence Liu Zi School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, People's Republic of China aut Song Xiaoning School of Internet of Things Engineering, Jiangnan University, 214122, Wuxi, People's Republic of China aut Tang Zhenmin School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, People's Republic of China aut Neural Computing and Applications Springer London 26/8(2015-11-01), 2013-2026 0941-0643 26:8<2013 2015 26 521 https://doi.org/10.1007/s00521-015-1863-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1863-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Zi School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, People's Republic of China aut NATIONALLICENCE 700 1- Song Xiaoning School of Internet of Things Engineering, Jiangnan University, 214122, Wuxi, People's Republic of China aut NATIONALLICENCE 700 1- Tang Zhenmin School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, People's Republic of China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 2013-2026 0941-0643 26:8<2013 2015 26 521