Author
Xingjie Zhu
Bio: Xingjie Zhu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Sparse approximation & Pattern recognition (psychology). The author has an hindex of 1, co-authored 1 publications receiving 154 citations.
Papers
More filters
TL;DR: This paper proposes to exploit the symmetry of the face to generate new samples and devise a representation based method to perform face recognition that outperforms state-of-the-art face recognition methods including the sparse representation classification (SRC), linear regression classification (LRC), collaborative representation (CR) and two-phase test sample sparse representation (TPTSSR).
160 citations
Cited by
More filters
TL;DR: A novel discriminative sparse representation method is proposed and its noticeable performance in image classification is demonstrated by the experimental results, and the proposed method outperforms the existing state-of-the-art sparse representation methods.
Abstract: Sparse representation has shown an attractive performance in a number of applications. However, the available sparse representation methods still suffer from some problems, and it is necessary to design more efficient methods. Particularly, to design a computationally inexpensive, easily solvable, and robust sparse representation method is a significant task. In this paper, we explore the issue of designing the simple, robust, and powerfully efficient sparse representation methods for image classification. The contributions of this paper are as follows. First, a novel discriminative sparse representation method is proposed and its noticeable performance in image classification is demonstrated by the experimental results. More importantly, the proposed method outperforms the existing state-of-the-art sparse representation methods. Second, the proposed method is not only very computationally efficient but also has an intuitive and easily understandable idea. It exploits a simple algorithm to obtain a closed-form solution and discriminative representation of the test sample. Third, the feasibility, computational efficiency, and remarkable classification accuracy of the proposed $l_{2}$ regularization-based representation are comprehensively shown by extensive experiments and analysis. The code of the proposed method is available at http://www.yongxu.org/lunwen.html .
171 citations
TL;DR: A novel method to automatically produce approximately axis-symmetrical virtual face images that is mathematically very tractable and quite easy to implement and verified in comparison with state-of-the-art dictionary learning algorithms.
111 citations
TL;DR: This paper proposes a scheme to produce the mirror image of the face and integrate the original face image and its mirror image for representation-based face recognition and shows that the proposed scheme can greatly improve the accuracy of the representation- based classification methods.
86 citations
TL;DR: The image classification experiments show that the simultaneous use of the proposed novel representations and original images can obtain a much higher accuracy?than the use of only the original images.
78 citations
TL;DR: Simulation results obtained with the Chokepoint video dataset indicate that the proposed method provides a significantly higher level of performance compared state-of-the-art systems when a single reference still per individual is available for matching.
60 citations