Gait recognition based on curvelet transform and PCANet

被引：1

作者：

Chhatrala R. ^{[1
]}

Jadhav D. ^{[2
]}

机构：

[1] Research Scholar, Rajarshi Sahu College of Engineering, Savitribai Phule Pune University, Pune

[2] Principal, Government Polytechnic, Ambad, Maharastra

来源：

Pattern Recognition and Image Analysis | 2017年 / 27卷 / 03期

关键词：

biometric; curvelet; gait recognition; PCANet;

D O I：

10.1134/S1054661817030075

中图分类号：

学科分类号：

摘要：

Conventional gait recognition schemes has poor recognition accuracies in presence of covariates. It is mainly due to ineffective and inefficient representation and discriminative feature extraction schemes. The paper presents new technique to extract discriminative features from masked gait energy image based on curvelet transform and PCANet. The binary gait silhouette video sequence obtained from pre-processing of video sequence is converted in to masked gait energy image and then direction and edge representation ability of fast discrete curvelet transform is employed. Nonlinear and non invertible, image space to feature space mapping scheme of PCANet is used to extract discriminative robust features. The suitability and effectiveness of newly proposed scheme is demonstrated by experimentation on standard publicly available benchmark USF HumanID database. © 2017, Pleiades Publishing, Ltd.

引用

页码：525 / 531

页数：6

共 39 条

[1]

Han J., Bhanu B., Individual recognition using gait energy image, IEEE Trans. Pattern Anal. Mach. Intellig., 28, (2006)

[2]

Cheung T.H.W.N.K., Liu J.N., Gait fow image:a silhouette-based gait representation for human identification, Pattern Recogn., 44, (2011)

[3]

Huang X., Boulgouris N.V., Gait recognition with shifted energy image and structural feature extraction, IEEE Trans. Image Processing, 21, (2012)

[4]

Liu Y., Collins R., Lee S., Shape variation-based frieze pattern for robust gait recognition, Proc. CVPR, pp. 1-8, (2007)

[5]

Bashir K., Xiang T., Gong S., Gait recognition using gait entropy image, Proc. Int. Conf. on Crime Detection and Prevention, (2009)

[6]

Makihara Y., Sagawa R., Mukaigawa Y., Echigo T., Yagi Y., Gait recognition using a view transformation model in the frequency domain, Proc. Computer Vision–ECCV 2006, pp. 151-163, (2006)

[7]

Xu D., Huang Y., Zeng Z., Xu X., Human gait recognition using patch distribution feature and locality-constrained group sparse representation, IEEE Trans. Image Processing, 21, (2012)

[8]

Xu D., Yan S., Tao D., Zhang L., Li X., Zhang H.-J., Human gait recognition with matrix representation, IEEE Trans. Circuits Syst.Video Technol., 16, (2006)

[9]

Lu H., Plataniotis K.N., Venetsanopoulos A.N., Mpca: multilinear principal component analysis of tensor objects, IEEE Trans. Neural Networks, 19, (2008)

[10]

Lu H., Plataniotis K.N., Venetsanopoulos A.N., Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning, IEEE Trans. Neural Networks, 20, (2009)

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