Pose Invariant Face Recognition using Hybrid DWT-DCT Frequency Features with Support Vector Machines

Face recognition is a challenging problem and up to date, there is no technique that provides a robust solution to all situations. This paper presents a hybrid approach to pose invariant human face recognition. The proposed scheme is based on a combination of the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) analysis on face images. The DWT-DCT domain coefficients are used for feature extraction using simple statistical measures and quantization. This approach reduces the dimension of the original face images while preserving the property of data distribution in the feature subspace. A Support vector machine (SVM) classifier is used for classifying DWT-DCT based feature vectors into separate groups for recognition purposes. The hybrid DWT-DCT-SVM face recognition model is evaluated in MATLAB on the Cambridge ORL face database. Comparison of the proposed technique with existing face recognition schemes proves that the combination of DWT-DCT improve feature selection performance compared to other approaches.