Color-Based Face Recognition with Different Color Spaces and Image Quality Assessment

Image quality is a critical issue for the recognition of faces because i.t reduces the risk of forging face recognition systems. The most relevant face spoofing attacks reported in previous studies follow o.ne of the three trends: mobile attacks, high-def attacks, or print attacks. Spoofing attacks have prompted the biometric research community to learn more about the threat posed by these kinds of attacks on many biometric traits such as face, fingerprint, iris, etc. In this thesis, various Image Quality Assessment techniques are used to detect image quality. Fake and real face images presented to biometric systems can also be detected by analyzing the image quality. In this context, No-Reference Image Quality Assessment measures such as Distortion Specific Measures (JQI, HLFI), Training Based Measure (BIQI) and Natural Scene Statistic Measure (NIQE) a.re implemented to analyze the quality of the face images. Three color spaces are employed to check the quality of images under various conditions. RGB, HSV and YCbCr color spaces are implemented for each of their channels separately and then the channel outputs are concatenated for each color space. The facial features are extracted using Principal Component Analysis (PCA), Local Binary Patterns (LBP) and Color Local Binary Patterns (ColorLBP) feature extraction methods for face recognition experiments. Moreover, we propose a general face recognition algorithm for low, medium and high quality face images. The experimental results are demonstrated on three publicly available face databases, namely Replay Attack, Faces94, and ColorFERET. Face recognition rates on all databases with all color spaces are presented using three aforementioned feature extraction methods. Finally, the proposed method results are demonstrated and compared with the existing systems. The experimental results are successful and encouraging for the proposed method. Keywords: Face recognition, color spaces, feature extraction, image quality assessment.