Pose invariant face recognition using dominant frequency based holistic features and statistical classifier

Doctor of Engineering Thesis In the Graduate School of Science and Technology KUMAMOTO UNIVERSITY In Title of Pose Invariant Face Recognition Using Dominant Frequency Based Holistic Features and Statistical Classifier by I Gede Pasek Suta Wijaya Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric system but also because there are many potential applications of the face recognition which range from humancomputer interactions to authentication, security, and surveillance. The published methods of face recognition can be categorized in to three groups (Zhao et al., 2003). Firstly holistic matching method, which uses the whole face region as raw input to the recognition system; secondly features based (structural) matching methods which use the local features such as the eyes, noses, and mouth, and local statistics (geometrics and or appearance) as data input to the recognition system; and thirdly hybrid methods which use both whole face and local features as data input to the system. The PCA-based and LDA-based methods of face recognition that belong to the first category are well known and encouraging results have been achieved. However, both of them have their limitations: large computational costs, high memory space requirement, and retraining problems. In addition, the main disadvantage is the PCA projected features are lack of discrimination power because it removes the null spaces of data scatter that have the discriminant information. This dissertation presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT), discrete wavelet transforms (DWT), and moment analysis as global features extraction of face images. In detail, our proposed methods can be categorized as follows: 1. Dominant frequency features based face recognition. The scheme of this method is based on frequency analysis to obtain the features of face image and multi-resolution metric to determine the similarity among the query face features and the training face features set. There are two main aims of the proposed method: to create holistic compact and meaningful features of face image without removing significant face image information, and to build a simple training algorithm that can solve the retraining problem of PCA-based face recognition. The proposed holistic features of face image are obtained by frequency analysis and quantization. The quantization will make the face features be not sensitive to small lighting variations. 2. Hybrid dominant frequency features based face recognition. Our previous work remains lack of discriminant power, which is shown by success rate still far from the maximum. It means the single frequency analysis is not adequately enough yet to represent the holistic information of any face pose variations. To address this problem, we propose an improvement of pose invariant human face recognition approach based on the analysis of the DCT and multi level Discrete Wavelet Transforms (DWT) of the face images. From both the DCT and DWT domain coefficients, which describe the face information, are fused as compact and meaningful features vector, using simple statistical measure and quantization called as the hybrid dominant frequency features. The main objective of the proposed method is to improve our previous method performance in term of recognition rate. 3. Statistical based face recognition. In order to improve the performance of our previous face recognition, we propose an features cluster which is derived from maximum a posteriori (MAP) discriminant called as modified LDA (MLDA). However, the propose features cluster does not works on quantized dominant frequency features because that features make the global covariance be singular. Therefore, we implements non quantized dominant frequency features. In this case, the MLDA is introduced to performed multi-pose face features cluster. There are several objectives of our proposed method. Firstly, to redefine our previous compact and meaningful face feature without removing significant face image information. Secondly, to build a simple classification technique that can classify face images to person’s classes well. Thirdly, to make the MLDA-based training system that can solve the retraining problem. Finally, to know the effectiveness of DCT and wavelet analysis as face feature extraction when they are combined with the MLDA based classification. In last of this section, our proposed method will work in color face image instead of grayscale in order to cover the skin information. The skin color face image is one of discriminant information which is available in the chrominance component and have to consider to get better performances. Consequently, it will take longer time processing than just working in grayscale. 4. Real-time face recognition using predictive LDA and alternative PCA. Previously, the global/holistic feature of face image, which is based on dominant frequency content, has been successfully implemented for face recognition. By using the global/holistic features concept as dimensional reduction of the face image could compress about 99.39% of the original size (i.e., less than 100 elements of 16384 elements) which gave good enough performance. It means the frequency analysis based global features is an efficient way for reducing the original data dimensional. Therefore, that holistic features concept, which is combined with the alternative PCA (APCA), predictive LDA (PDLDA), and integration of them, is implemented to create real-time face recognition. This proposed method is an alternative approach to face recognition algorithm that is based on global/holistic features of face image, which is combined with APCA and PDLDA to overcome large computational costs and retraining problem of the conventional PCA and LDA and with integration of APCA and PDLDA to improve features cluster. There are several objectives of the proposed method. Firstly, to prove that the global features of face image contains most of face classification information. Secondly, to define alternative PCA and redefine the predictive between class scatter (Sb) which have the same structure as their original but they have less computation complexity. Thirdly, to optimize the recognition performance using multi-stage classifier integration. Finally, to know the effectiveness of proposed methods compared with the best traditional subspace methods. In addition, the proposed method also work in color face image instead of grayscale to cover the skin information. Furthermore, shape face analysis performed by invariant moments is also included to get the holistic information of face pose variations. In order to know the performance of our proposed methods, the experiments were carried out using several challenging face databases: the ORL database, YALE database, ITS-Lab. Kumamoto University database, INDIA database, and FERET database. Each database has special characteristics. From all of the experimental results, the proposed methods have proved our hypothesis as described: firstly, the global features as a dimensional reduction of raw image has been successfully implemented with good achievement when Lq metric, MLDA, one-dimensional APCA, DLDA, and PDLDA as features clustering; secondly, PDLDA-based face recognition has been proved that they require less time processing for training and retraining; thirdly, the optimum recognition performance can be achieved by integrating the PDLDA and APCA or DLDA which the almost the same time processing; and the rest, the proposed method (APCA and PDLDA) outperforms over the recent sub-space methods (DLDA, 2DLDA, (2D)DLDA, 2DPCA, and (2D)2DPCA. ) and the PDLDA can used as alternative solution to avoid recalculating the Sb and global mean of the retraining process of DLDA. In addition, the PDLDA is alternative solution for large size data clustering because it does not depend on the global means. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.