Abstract
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.
Highlights
Robust and accurate face recognition (FR) is one of the most important problems in computer vision applications
A recent method has been proposed by the authors of References [3,54], wherein, they improve the rate of FR recognition accuracy by using the symmetry property of the face, to using Symmetry for Collaborative Representation-Based Classification (SCRC)
This paper presents an effective method to overcome the restricted number of training sets using the property of face symmetry
Summary
Robust and accurate face recognition (FR) is one of the most important problems in computer vision applications. Enhancement from the list could be combined [8,12], such as LBP with GLCM, in order to make the feature This operation was accomplished by a preprocessing step using well-known techniques, extraction operation more robust. The images of the face were enhanced, before extracting their namely the Gaussian low-pass filter (GLPF) [13], Difference of Gaussian (DoG) [14], and the Discrete features. This enhancement operation was accomplished by a preprocessing step using well-known.
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