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SpPCANet: a simple deep learning-based feature extraction approach for 3D face recognition

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Abstract

A Sparse Principal Component Analysis Network (SpPCANet) based feature extraction is proposed here for 3D face recognition. The network consists of three basic components: (1) Multistage sparse principal component analysis filters, (2) Binary hashing, and (3) Block-wise histogram computation. Here, the sparse principal component analysis is used to learn multistage filter banks at the convolution stage, which is followed by binary hashing for indexing and block-wise histogram for pooling. Finally, a linear support vector machine (SVM) is used for classifying the features extracted by SpPCANet. The proposed network SpPCANet is a lightweight deep learning network. Three well-known 3D face databases, namely, Frav3D, Bosphorus3D, and Casia3D, are used for validating the proposed system. This proposed network has been extensively studied by varying different parameters, such as the number of filters at the convolution layer and the size of filters at the convolution layer and size of non-overlapping blocks at the pooling layer. Handling all types of variation of faces available in Frav3D, Bosphorus3D, and Casia3D databases, the system has acquired 96.93%, 98.54%, and 88.80% recognition rates, respectively.

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Acknowledgments

The first author is grateful to the Ministry of Electronics and Information Technology (MeitY), Govt. of India, for the grant of the Visvesvaraya doctorate fellowship award. The authors are also thankful to CMATER laboratory of the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India, for providing the necessary infrastructure for this work.

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  1. Computer Science and Engineering, Jadavpur University, 188, Raja S. C. Maulik Road, Kolkata, 700032, India

    Koushik Dutta, Debotosh Bhattacharjee & Mita Nasipuri

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  1. Koushik Dutta
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Correspondence to Koushik Dutta.

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Dutta, K., Bhattacharjee, D. & Nasipuri, M. SpPCANet: a simple deep learning-based feature extraction approach for 3D face recognition. Multimed Tools Appl 79, 31329–31352 (2020). https://doi.org/10.1007/s11042-020-09554-6

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