Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. The algorithm is applied to coefficients extracted by two feature extraction techniques: the discrete cosine transforms (DCT) and the discrete wavelet transform (DWT). The proposed PSO-based feature selection algorithm is utilized to search the feature space for the optimal feature subset where features are carefully selected according to a well defined discrimination criterion. Evolution is driven by a fitness function defined in terms of maximizing the class separation (scatter index). The classifier performance and the length of selected feature vector are considered for performance evaluation using the ORL face database. Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.

Face recognition using particle swarm optimization-based selected features

To render the face recognition work more efficiently, ACOSVM, a face recognition system combining Ant Colony Optimization (ACO) with Support Vector Machine (SVM), is presented, which employs SVM classifier with the optimal features selected by ACO. The Principal Component Analysis method (PCA) is used to extract eigenfaces from images at the preprocessing stage, and then ACO for selection of the optimal subset features using cross-validation is described, which can be considered as wrapper approach in the feature selection algorithms. The experiments indicate that the proposed face recognition system with selected features is more practical and efficient when compared with others. And the results also suggest that it may find wide applications in the pattern recognition.

Ant Colony Optimization for Feature Selection in Face Recognition

Accurate recognition of facial expression is a challenging problem especially from multi-scale and multi orientation face images. In this article, we propose a novel technique called Weber Local Binary Image Cosine Transform (WLBI-CT). WLBI-CT extracts and integrates the frequency components of images obtained through Weber local descriptor and local binary descriptor. These frequency components help in accurate classification of various facial expressions in the challenging domain of multi-scale and multi-orientation facial images. Identification of significant feature set plays a vital role in the success of any facial expression recognition system. Effect of multiple feature sets with varying block sizes has been investigated using different multi-scale images taken from well-known JAFEE, MMI and CK+ datasets. Extensive experimentation has been performed to demonstrate that the proposed technique outperforms the contemporary techniques in terms of recognition rate and computational time.

Reliable facial expression recognition for multi-scale images using weber local binary image based cosine transform features

In this paper, we propose embedded face recognition (FR) to use in intelligent image system. For efficient FR VLSI design, we use a feature selection and feature extraction method based on Gabor wavelets using a fast genetic algorithm (FGA). Many FR systems are based on Gabor wavelet due to its desirable characteristics of spatial locality and orientation selectivity. However, the process of searching for features with Gabor wavelet is computationally expensive and has an unusual sensibility for variations such as illumination. To overcome these problems and use in real-time applications, we optimize Gabor wavelet's parameters of translation, orientations and scales, which make it approximates a local image contour region by the use of hardware oriented FGA. From experimental results, we certify that our method shows recognition rate of over 97.27 % for FERET dataset, which exceeds the performance of the other popular methods

Embedded face recognition based on fast genetic algorithm for intelligent digital photography

Based on the application of DCT (discrete cosine transform) in the image compression, the feasibility of DCT to be used in image feature dimension reduction is analyzed, and the basic principle of the image feature dimension reduction based on DCT is given in this paper. And then, taking the face recognition and the facial expression recognition as the research background, the theoretical analysis that DCT algorithm has the higher recognition than 2DPCA (two-dimensional principal component analysis) in the face recognition and the facial expression recognition is given under the condition that DCT and 2DPCA algorithms have the approximate dimension reduction effect. At last, the comparative simulation experiment is performed on DCT and 2DPCA algorithms respectively by use of the AT&T face database and JAFFE facial expression database.

Comparative study of dimension reduction and recognition algorithms of DCT and 2DPCA

Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step for classification algorithm. A face recognition approach based on KPCA and genetic algorithms (GAs) is proposed. By the use of the polynomial functions as a kernel function in KPCA, the high order relationships can be utilized and the nonlinear principal components can be obtained. After we obtain the nonlinear principal components, we use GAs to select the optimal feature set for classification. At the recognition stage, we employed linear support vector machines (SVM) as classifier for the recognition tasks. Two face databases were used to test our algorithm and higher recognition rates were obtained which show that our algorithm is effective.

Face recognition using kernel principal component analysis and genetic algorithms

http://ieeexplore.ieee.org/iel5/5971804/6077785/06077807.pdf

Zhang Yankun

Papers

Face recognition using kernel principal component analysis and genetic algorithms

Efficient face recognition method based on DCT and LDA