Deep Sparse Representation Classifier for facial recognition and detection system

Eric Juwei Cheng, Kuang Pen Chou, Shantanu Rajora, Bo Hao Jin, M. Tanveer, Chin Teng Lin, Ku Young Young, Wen Chieh Lin, Mukesh Prasad*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets.

Original languageEnglish
Pages (from-to)71-77
Number of pages7
JournalPattern Recognition Letters
StatePublished - 1 Jul 2019


  • Convolutional Neural Network
  • Deep learning
  • Face recognition
  • Feature extraction
  • Sparse Representation Classifier


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