Multi-Resolution Analysis Features followed by Facial Part Detection for Face Recognition
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Abstract
Face recognition is considered the main physiological and behavioral biometrics due to the following advantages; simplicity of the face capturing, feature uniqueness and distinctness, and availability of the image acquisition devices. In this paper, a new approach to face recognition system (FRS) utilizing the Two-Dimensional Discrete Multi-Wavelets Transform (2D DMWT) followed by Vector Quantization (VQ) to the detected face and facial parts (DF and DFP) is proposed. Faces and facial parts (Nose, Mouth, Left-Right Eyes) are detected in the preprocessing step. Face and facials are the main parts that represent each person in the feature extraction step. For dimensionality reduction and features selection, the 1-level of 2D DMWT decomposition is employed in the two representations. For each person in the second representation, four groups are constructed using the training poses, each group for each facial part. Furthermore, VQ and Kekre Fast Codebook Generation (KFCG) are applied to the detected faces and the four groups derived from the first and second representations, respectively. The Euclidean distance is utilized in the classification phase. Four databases, namely, YALE, FERET, FEI, and Georgia Tech. are used to test the FRS. These databases have different facial diversity, such as pose rotation, light condition, expressions, etc. K-fold Cross-Validation (CV) is utilized to analyze the experimental results. The proposed system improves the recognition rates and the storage requirement compared to the state-of-the-art approaches.
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