Alaa Eleyan

Complex wavelet transform-based face recognition

Complex approximately analytic wavelets provide a local multiscale description of images with good directional selectivity and invariance to shifts and in-plane rotations. Similar to Gabor wavelets, they are insensitive to illumination variations and facial expression changes. The complex wavelet transform is, however, less redundant and computationally efficient. In this paper, we first construct complex approximately analytic wavelets in the single-tree context, which possess Gabor-like characteristics. We, then, investigate the recently developed dual-tree complex wavelet transform (DT-CWT) and the single-tree complex wavelet transform (ST-CWT) for the face recognition problem. Extensive experiments are carried out on standard databases. The resulting complex wavelet-based feature vectors are as discriminating as the Gabor wavelet-derived features and at the same time are of lower dimension when compared with that of Gabor wavelets. In all experiments, on two well-known databases, namely, FERET and ORL databases, complex wavelets equaled or surpassed the performance of Gabor wavelets in recognition rate when equal number of orientations and scales is used. These findings indicate that complex wavelets can provide a successful alternative to Gabor wavelets for face recognition.

PCA and LDA based Neural Networks for Human Face Recognition

After 9/11 tragedy, governments in all over the world started to look more seriously to the levels of security they have at their airports and borders. Countries annual budgets were increased drastically to have the most recent technologies in identification, recognition and tracking of suspects. The demand growth on these applications helped researchers to be able to fund their research projects. One of most common biometric recognition techniques is face recognition. Although face recognition is not as accurate as the other recognition methods such as fingerprints, it still grabs huge attention of many researchers in the field of computer vision. The main reason behind this attention is the fact that the face is the conventional way people use to identify each others. Over the last few decades, a lot of researchers gave up working in the face recognition problem due to the inefficiencies of the methods used to represent faces. The face representation was performed by using two categories. The First category is

PCA and LDA based face recognition using feedforward neural network classifier

Principal component analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are among the most common feature extraction techniques used for the recognition of faces. In this paper, two face recognition systems, one based on the PCA followed by a feedforward neural network (FFNN) called PCA-NN, and the other based on LDA followed by a FFNN called LDA-NN, are developed. The two systems consist of two phases which are the PCA or LDA preprocessing phase, and the neural network classification phase. The proposed systems show improvement on the recognition rates over the conventional LDA and PCA face recognition systems that use Euclidean Distance based classifier. Additionally, the recognition performance of LDA-NN is higher than the PCA-NN among the proposed systems.

Gabor wavelet transform based facial expression recognition using PCA and LBP

This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis (PCA) and Local binary pattern (LBP) algorithms. Experiments were carried out of using Japanese female facial expression (JAFFE) database. In all experiments conducted using JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with an average recognition rate of 90% under the same experimental setting.

Face recognition system based on PCA and feedforward neural networks

Face recognition is one of the most important image processing research topics which is widely used in personal identification, verification and security applications. In this paper, a face recognition system, based on the principal component analysis (PCA) and the feedforward neural network is developed. The system consists of two phases which are the PCA preprocessing phase, and the neural network classification phase. PCA is applied to calculate the feature projection vector of a given face which is then used for face identification by the feedforward neural network. The proposed PCA and neural network based identification system provides improvement on the recognition rates, when compared with a face classifier based on the PCA and Euclidean Distance.

Co-occurrence based statistical approach for face recognition

This paper introduces a new face recognition method based on the gray-level co-occurrence matrix (GLCM). Both distributions of the intensities and information about relative position of neighbourhood pixels are carried by GLCM. Two methods have been used to extract feature vectors from the GLCM for face classification. The first, method extracts the well-known Haralick features to form the feature vector, where the second method directly uses GLCM by converting the matrix into a vector which can be used as a feature vector for the classification process. The results demonstrate that using the GLCM directly as the feature vector in the recognition process outperforms the feature vector containing the statistical Haralick features. Additionally, the proposed GLCM based face recognition system outperforms the well-known techniques such as principal component analysis and linear discriminant analysis.

Facial expression recognition using Support Vector Machines

This paper propose a facial expression recognition approach based on Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. Experiments were carried out on the Japanese Female Facial Expression (JAFFE) database and our recently introduced Mevlana University Facial Expression (MUFE) database. Support Vector Machine (SVM) was used as classifier. In all conducted experiments on JAFFE and MUFE databases, obtained results reveal that PCA+SVM has an average recognition rate of 87% and 77%, respectively.

Face Recognition using Multiresolution PCA

In this paper we develop two techniques for face recognition using the idea of multiresolution face recognition. The multiresolution subbands are generated by using wavelet transform. The first technique is called the Multiresolution Feature Concatenation (MFC), where we use principal component analysis (PCA) as a dimensional reduction approach on each subband then concatenate the resulting projection coefficients of each subband together and perform classification. The second technique is called the Multiresolution Majority Voting (MMV), where the PCA approach and the classification are done separately on each subband and then the majority voting is applied for making decision. Both techniques show promising results and MMV approach outperforms the MFC approach. Moreover, the two techniques outperform the conventional PCA approach.

Image Feature Extraction Using Compressive Sensing

Summary. In this paper a new approach for image feature extraction is presented. We used the Compressive Sensing (CS) concept to generate the measurement matrix. The new measurement matrix is different from the measurement matrices in literature as it was constructed using both zero mean and nonzero mean rows. The image is simply projected into a new space using the measurement matrix to obtain the feature vector. Another proposed measurement matrix is a random matrix constructed from binary entries. Face recognition problem was used as an example for testing the feature extraction capability of the proposed matrices. Experiments were carried out using two well-known face databases, namely, ORL and FERET databases. System performance is very promising and comparable with the classical baseline feature extraction algorithms.

Discrete wavelet transform-based Ant Colony Optimization for edge detection

Ant Colony Optimization (ACO) is used to obtain the edges of an image which is acquired from sampling and quantization of a continuous image. Such techniques generate a pheromone matrix that represents the edge information at each pixel position on the routes formed by ants dispatched on the image. However, when the image is buried in noise, ACO performance deteriorates. In this paper, we propose to use discrete wavelet transform (OWT) as a preprocessing step with ACO to enhance image edge detection. The proposed algorithm creates a pheromone matrix to stand for the edges of the low frequency component obtained from the OWT decompositions, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of ants are driven by the local variation of the images intensity values. The proposed approach shows a significant performance and capability of detecting edges superior to existing techniques.