Joint representation classification for collective face recognition - Wang, Liping; Chen, Songcan (Pattern recognition, 2017)
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.