Subspace cross representation measure for robust face recognition with few samples. (September 2022) - Zhang, Jian; Qin, Xin; Xiao, Yuchen; Fei, Rong; Zang, Qiyan; Xu, Shuai; Bo, Liling; Li, Hongran; Zhang, Heng; Zhong, Zhaoman
Abstract: Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition.