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Deep Learning Based Face Recognition with Sparse Representation Classification

  • Eric-Juwei Cheng
  • Mukesh Prasad
  • Deepak Puthal
  • Nabin Sharma
  • Om Kumar Prasad
  • Po-Hao Chin
  • Chin-Teng Lin
  • Michael Blumenstein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)

Abstract

Feature extraction is an essential step in solving real-world pattern recognition and classification problems. The accuracy of face recognition highly depends on the extracted features to represent a face. The traditional algorithms uses geometric techniques, comprising feature values including distance and angle between geometric points (eyes corners, mouth extremities, and nostrils). These features are sensitive to the elements such as illumination, variation of poses, various expressions, to mention a few. Recently, deep learning techniques have been very effective for feature extraction, and deep features have considerable tolerance for various conditions and unconstrained environment. This paper proposes a two layer deep convolutional neural network (CNN) for face feature extraction and applied sparse representation for face identification. The sparsity and selectivity of deep features can strengthen sparseness for the solution of sparse representation, which generally improves the recognition rate. The proposed method outperforms other feature extraction and classification methods in terms of recognition accuracy.

Keywords

Feature extraction Face recognition Deep learning Convolutional neural network Classification 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Eric-Juwei Cheng
    • 1
  • Mukesh Prasad
    • 2
  • Deepak Puthal
    • 3
  • Nabin Sharma
    • 2
  • Om Kumar Prasad
    • 4
  • Po-Hao Chin
    • 1
  • Chin-Teng Lin
    • 2
  • Michael Blumenstein
    • 2
  1. 1.Department of Electrical EngineeringNational Chiao Tung UniversityHsinchuTaiwan
  2. 2.Centre for Artificial Intelligence, School of Software, FEITUniversity of Technology SydneySydneyAustralia
  3. 3.School of Electrical and Data Engineering, FEITUniversity of Technology SydneySydneyAustralia
  4. 4.International College of Semiconductor TechnologyNational Chiao Tung UniversityHsinchuTaiwan

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