Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

Symmetry

OsmanSerdar Gedik¹  Javad Rahebi²  Saad Allagwail³ 

[1]Computer Engineering, Ankara Yildirim Beyazit University, Ankara 06010, Turkey

[2]Department of Computer Engineering, Ankara Yildirim Beyazit University, Ankara 06010, Turkey

[3]

[4]Department of Electrical &

关键词: face recognition;    symmetry;    Wavelet Transform;    Local Binary Pattern;    Gray-Level Co-Occurrence Matrix;    Gabor;   

DOI  :  10.3390/sym11020157

来源: DOAJ

【 摘 要 】

In the practical reality of face recognition applications, the human face can have only a limited number of training images. However, it is known that, in general, increasing the number of training images also increases the performance of face recognition systems. In this case, a new set of training samples can be generated from the original samples, using the symmetry property of the face. Although many face recognition methods have been proposed in the literature, a robust face recognition system is still a challenging task. In this paper, recognition performance was improved by using the property of face symmetry. Moreover, the effects of illumination and pose variations were reduced. A Two-Dimensional Discrete Wavelet Transform, based on the Local Binary Pattern, which is a new approach for face recognition using symmetry, has been presented. The method has three main stages, preprocessing, feature extraction, and classification. A Two-Dimensional Discrete Wavelet Transform with Single-Level and Gaussian Low-Pass Filter were used, separately, for preprocessing. The Local Binary Pattern, Gray Level Co-Occurrence Matrix, and the Gabor filter were used for feature extraction, and the Euclidean Distance was used for classification. The proposed method was implemented and evaluated using the Olivetti Research Laboratory (ORL) and Yale datasets. This study also examined the importance of the preprocessing stage in a face recognition system. The experimental results showed that the proposed method had a recognition accuracy of 100%, for both the ORL and Yale datasets, and these recognition rates were higher than the methods in the literature.

【 授权许可】

Unknown