Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification

• Published in: Pattern recognition 2016-06, Vol.54, p.68-82
• Main Authors: Xu, Yong, Zhang, Zheng, Lu, Guangming, Yang, Jian
• Format: Article
• Language: English
• Subjects: Algorithms; Approximately symmetrical face; Classification; Face image preprocessing; Face recognition; Image classification; Mathematical analysis; Matlab; Preprocessing; Sparse representation; Virtual sample
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2015.12.017

Though most of the faces are axis-symmetrical objects, few real-world face images are axis-symmetrical images. In the past years, there are many studies on face recognition, but only little attention is paid to this issue and few studies to explore and exploit the axis-symmetrical property of faces for face recognition are conducted. In this paper, we take the axis-symmetrical nature of faces into consideration and design a framework to produce approximately axis-symmetrical virtual dictionary for enhancing the accuracy of face recognition. It is noteworthy that the novel algorithm to produce axis-symmetrically virtual face images is mathematically very tractable and quite easy to implement. Extensive experimental results demonstrate the superiority in face recognition of the virtual face images obtained using our method to the original face images. Moreover, experimental results on different databases also show that the proposed method can achieve satisfactory classification accuracy in comparison with state-of-the-art image preprocessing algorithms. The MATLAB code of the proposed method can be available at http://www.yongxu.org/lunwen.html. •Developed a novel method to automatically produce approximately axis-symmetrical virtual face images.•Treated as an effective image preprocessing method.•Used as a virtual image dictionary learning method for image classification.•Extensive experiments on different face databases show its effectiveness as an image preprocessing algorithm.•The strong identification capability of our method is verified in comparison with state-of-the-art dictionary learning algorithms.