Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969813 | Pattern Recognition | 2017 | 11 Pages |
Abstract
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,qâl2,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 qâ[1,2] and pâ(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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Liping Wang, Songcan Chen,