Joint representation classification for collective face recognition

• Published in: Pattern recognition Vol. 63; pp. 182 - 192
• Main Authors: Wang, Liping, Chen, Songcan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2017
• Subjects: IQM; JRC; Practical IQM; SRC; Practical IQM; JRC; IQM; SRC
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2016.10.004

In recent years, many representation based classifications have been proposed and widely used in face recognition. However, these methods code and classify testing images separately even for image-set of the same subject. This scheme utilizes only an individual representation rather than the collective one to classify such a set of images, doing so obviously ignores the correlation among the given set of images. In this paper, a joint representation classification (JRC) for collective face recognition is presented. JRC takes the correlation of multiple images as well as a single representation into account. Even for an image-set mixed with different subjects, JRC codes all the testing images over the base images simultaneously to facilitate recognition. To this end, the testing images are aligned into a matrix and the joint representation coding is formulated as a generalized l2,q−l2,p matrix minimization problem. A unified algorithm, named by iterative quadratic method (IQM), and its practical implementation are developed specially to solve the induced optimization problem for any q∈[1,2] and p∈(0,2]. Experimental results on three public databases show that the JRC with practical IQM not only saves much computational cost but also achieves better performance in collective face recognition than state-of-the-art methods. •A joint representation classification (JRC) is presented.•JRC codes the collective images simultaneously.•Joint representation coding is formulated to a mixed l2,q−l2,p minimization.•A unified method is developed and its convergence is established.•Experiments confirm the collective face recognition performance of JRC over state-of-the-arts.