Face Representation Using Combined Method of Gabor Filters, Wavelet Transformation and DCV and Recognition Using RBF

Abstract

An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.

New Method of Content Based Image Retrieval based on 2-D ESPRIT Method and the Gabor Filters

“…The advantage of these filters is that their functioning is close to the human visual treatments, and they have the advantage of being programmable in frequency and in orientation. Indeed, Gabor filters find their place in several areas such as: segmentation [4], pattern recognition [5,6], classification [7,8], content based image retrieval [9,10]. The texture parameters are determined by calculating the average and the variance of the image filtered by Gabor filter.…”

Face Recognition: A Survey

“…Redundancy is present in Gabor features because these features are usually high dimensional data [74] and sometimes overlapping occurs between the supports of Gabor filters that result in redundancy of information of features [75]. Feature reduction can be done using Gabor Wavelet transformation method [76]. Face recognition can also be done by using Gabor features in the global form [77][78].…”

“…The Network in the PNN is divided into subnets because its network is not completely connected. Self-Organizing Map Neural Network (SOM) [75][76][77][78] having the property of topological preservation is an artificial neural network used in face recognition. SOM is also known as Kohonen Map.…”