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Gait recognition based on curvelet transform and PCANet

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Abstract

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.

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Author information

Authors and Affiliations

  1. Research Scholar, Rajarshi Sahu College of Engineering, Savitribai Phule Pune University, Pune, India

    R. Chhatrala

  2. Principal, Government Polytechnic, Ambad, Maharastra, India

    D. Jadhav

Authors
  1. R. Chhatrala
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  2. D. Jadhav
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Corresponding author

Correspondence to R. Chhatrala.

Additional information

The article is published in the original.

Risil Chhatrala is a research scholar at J.S.P.M., Rajarshi Sahu College of Engineering and Research, Savitribai Phule Pune University. He has published 2 research paper in reputed international journals and 3 papers in other international journals in the field of Image/Video Processing and Pattern Recognition. His research interests include image and video processing, pattern recognition.

Dattatray V. Jadhav is a well-known author in the field of the journal scope. Currently, he is Principal, Government Polytechnic, Ambad, Maharastra, India. His research interests include signal processing, pattern recognition, image and video processing.

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Chhatrala, R., Jadhav, D. Gait recognition based on curvelet transform and PCANet. Pattern Recognit. Image Anal. 27, 525–531 (2017). https://doi.org/10.1134/S1054661817030075

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  • DOI: https://doi.org/10.1134/S1054661817030075

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