Gabor filter bank with deep autoencoder based face recognition system

These days, face recognition systems are widely being employed in various daily applications such as smart phone unlocking, tracking school attendance, and secure online bank transactions, smarter border control, to name a few. In spite of the remarkable progress, face recognition systems still suffer from occlusions, light variations, camera types and their resolutions. Face recognition is still a dynamic research field. In this paper, we propose an efficient face recognition system based on Gabor filter bank and a deep learning method known as Sparse AutoEncoder (SAE). The main aim of the proposed system is to improve the features extracted by Gabor filter bank using SAE method. Then, these enhanced features are subjected to features reduction using principal component analysis and linear discriminant analysis (PCA + LDA) technique. Finally, the matching stage is accomplished via cosine Mahalanobis distance. Experiments on seven publicly available databases (i.e., JAFFE, AT & T, Yale, Georgia Tech, CASIA, Extended Yale, Essex) show that the proposed system can achieve promising results with the combination of Gabor and SAE, as well as outperform previously proposed methods.

Automated summary

This paper proposes an efficient face recognition system based on Gabor filter bank and Sparse AutoEncoder (SAE) deep learning method. By improving feature extraction and reduction techniques, the proposed system achieves promising results on multiple databases and outperforms previous methods.