LWKPCA: A New Robust Method for Face Recognition Under Adverse Conditions
نویسندگان
چکیده
Over the last two decades, face recognition (FR) has become one of most prevailing biometric applications for effective people identification as it offers practical advantages over other modalities. However, current state-of-the-art findings suggest that FR under adverse and challenging conditions still needs improvements. This is because images can contain many variations like expression, pose, illumination. To overcome effect these challenges, necessary to use representative features using feature extraction methods. In this paper, we present a new method robust called Local Binary Pattern Wavelet Kernel PCA (LWKPCA). The proposed aims extract discriminant information minimize errors. obtained first by best nonlinear projection algorithm RKPCA. Then, adapted reduce dimensionality extracted Color Wavelets transformation LBP Descriptor. general idea our descriptor find representation image in vector structure novel grouping strategy generated Three-Level decomposition Discrete Transform (2D-DWT) (LBP). Extensive experiments on four well-known datasets namely ORL, GT, LFW, YouTube Celebrities show accuracy 100% 96.84% 99.34% 95.63% Celebrities.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3184616