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*

*此作品的通信作者

研究成果: Article同行評審

27 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)71-77
頁數7
期刊Pattern Recognition Letters
125
DOIs
出版狀態Published - 1 七月 2019

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