Weighted average integration of sparse representation and collaborative representation for robust face recognition

被引：0

作者：

Zeng S. ^{[1
]}

Xiong Y. ^{[1
]}

机构：

[1] Huizhou University, Guangdong

来源：

Comput. Vis. Media | / 4卷 / 357-365期

基金：

中国国家自然科学基金;

关键词：

collaborative representation; face recognition; image classification; sparse representation;

D O I：

10.1007/s41095-016-0061-5

中图分类号：

学科分类号：

摘要：

Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification. As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition. The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10% improvement in recognition accuracy. © 2016, The Author(s).

引用

页码：357 / 365

页数：8

共 39 条

[11]

Zhong D., Zhu P., Han J., Li S., An improved robust sparse coding for face recognition with disguise, International Journal of Advanced Robotic Systems, 9, (2012)

[12]

Xu Y., Zhu Q., Zhang D., Combine crossing matching scores with conventional matching scores for bimodal biometrics and face and palmprint recognition experiments, Neurocomputing, 74, 18, pp. 3946-3952, (2011)

[13]

Yang A.Y., Zhou Z., Balasubramanian A.G., Sastry S.S., Ma Y., Fast l <sub>1</sub>-minimization algorithms for robust face recognition, IEEE Transactions on Image Processing, 22, 8, pp. 3234-3246, (2013)

[14]

Mairal J., Bach F., Ponce J., Sapiro G., Zisserman A., Discriminative learned dictionaries for local image analysis, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, (2008)

[15]

Gao S., Tsang I.W.-H., Chia L.-T., Kernel sparse representation for image classification and face recognition, Computer Vision—ECCV 2010, pp. 1-14, (2010)

[16]

Yang J., Wright J., Huang T., Ma Y., Image super-resolution as sparse representation of raw image patches, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, (2008)

[17]

Zhang Z., Li Z., Xie B., Wang L., Chen Y., Integrating globality and locality for robust representation based classification, Mathematical Problems in Engineering, (2014)

[18]

Zhang L., Yang M., Feng X., Sparse representation or collaborative representation: Which helps face recognition, Proceedings of IEEE International Conference on Computer Vision, pp. 471-478, (2011)

[19]

Zhang L., Yang M., Feng X., Ma Y., Zhang D., Collaborative representation based classification for face recognition, (2012)

[20]

Chen X., Ramadge P.J., Collaborative representation, sparsity or nonlinearity: What is key to dictionary based classification, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processin, pp. 5227-5231, (2014)

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