Gabor Features For Face Recognition Research Articles

Ensemble of Log-Euclidean Kernel SVM based on Covariance Descriptors of Multiscale Gabor Features for Face Recognition

Face recognition (FR) is a challenging computer vision task due to various adverse conditions. Local features play an important role in increasing the recognition rate of an FR method. In this direction, the covariance descriptors of Gabor wavelet features have been one of the most prominent methods for accurate FR. Most existing methods rely on covariance descriptors of Gabor magnitude features extracted from single-scale face images. This study proposes a new method named multiscale Gabor covariance-based ensemble Log-euclidean SVM (MGcov-ELSVM) for FR that uses the covariance descriptors of Gabor magnitude and phase features derived from multiscale face representations. MGcov-ELSVM begins by producing multiscale face representations. Gabor magnitude and phase features are derived from the multiscale face images in the second stage. After that, the Gabor magnitude and phase features are used to generate covariance descriptors. Finally, Covariance descriptors are classified via a log-Euclidean SVM classifier, and a majority voting technique determines the final recognition results. The experimental results from two face databases, ORL and Yale, indicate that the MGcov-ELSVM outperforms some recent FR methods.

Locally lateral manifolds of normalised Gabor features for face recognition

Due to inherent characteristics of multiscale and orientation, normalised Gabor features have been successfully used in face recognition. Various previous works have showcased the strength and feasibility of this approach, especially on its robustness against local variations. However, the projected features are numerous and substantial in dimension, which is largely due to the convolution of multiscale and orientation of wavelets. Such features, when used in practical face recognition, would require relatively lengthy classification process, particularly when it involves computationally extensive local classifier or experts, such as ensembles of local cosine similarity (ELCS) classifier. The authors address this issue by simultaneously reducing the size of Gabor features laterally and locally using a manifold learning method called locally linear embedding (LLE). This method is thus denoted as locally lateral normalised local Gabor feature vector with LLE (LGFV/LN/LLE). Results on several publicly available face datasets reveal the superiority of the authors’ approach in terms of improvements in feature compression of LGFV features by up to a reduction of 95% of total dimensionality while increasing the average classification accuracy by 26%. Altogether, the authors show that their LGFV/LN/LLE augmented by ELCS classifiers delivers equivalent result when compared against the state‐of‐the‐art.

Multi-resolution elongated CS-LDP with Gabor feature for face recognition

Centre-symmetric local derivative pattern CS-LDP algorithm is proposed to describe the local second-order derivative feature of texture. However, CS-LDP can only describe second-order derivative feature of texture on four directions and lost some discriminant information on other directions. Addressing such problems, this paper proposed multi-resolution elongated CS-LDP ME-CS-LDP to solve such problem. By increasing the number of directions, which can be implemented by increasing the sampling points on the ellipse radius with interpolation, multi-resolution elongated CS-LDP can provide more discriminant information on more directions. Furthermore, our proposed multi-resolution elongated CS-LDP is defined in ellipse field to depict some important ellipse part of faces, like eyes and mouth. Gabor filter plus ME-CS-LDP/weighed ME-CS-LDP is used for face recognition in this paper. The experiment results on the illumination subset of Yale B database, the subset of PIE illumination database and VALID face database have validated the effectiveness of the proposed method.

Band-Reweighed Gabor Kernel Embedding for Face Image Representation and Recognition.

Face recognition with illumination or pose variation is a challenging problem in image processing and pattern recognition. A novel algorithm using band-reweighed Gabor kernel embedding to deal with the problem is proposed in this paper. For a given image, it is first transformed by a group of Gabor filters, which output Gabor features using different orientation and scale parameters. Fisher scoring function is used to measure the importance of features in each band, and then, the features with the largest scores are preserved for saving memory requirements. The reduced bands are combined by a vector, which is determined by a weighted kernel discriminant criterion and solved by a constrained quadratic programming method, and then, the weighted sum of these nonlinear bands is defined as the similarity between two images. Compared with existing concatenation-based Gabor feature representation and the uniformly weighted similarity calculation approaches, our method provides a new way to use Gabor features for face recognition and presents a reasonable interpretation for highlighting discriminant orientations and scales. The minimum Mahalanobis distance considering the spatial correlations within the data is exploited for feature matching, and the graphical lasso is used therein for directly estimating the sparse inverse covariance matrix. Experiments using benchmark databases show that our new algorithm improves the recognition results and obtains competitive performance.

Discriminant analysis of the two-dimensional Gabor features for face recognition

A new technique called two-dimensional Gabor Fisher discriminant (2DGFD) is derived and implemented for image representation and recognition. In this approach, the Gabor wavelets are used to extract facial features. The principal component analysis (PCA) is applied directly on the Gabor transformed matrices to remove redundant information from the image rows and a new direct two-dimensional Fisher linear discriminant (direct 2DFLD) method is derived in order to further remove redundant information and form a discriminant representation more suitable for face recognition. The conventional Gabor-based methods transform the Gabor images into a high-dimensional feature vector. However, these methods lead to high computational complexity and memory requirements. Furthermore, it is difficult to analyse such high-dimensional data accurately. The novel 2DGFD method was tested on face recognition using the ORL, Yale and extended Yale databases, where the images vary in illumination, expression, pose and scale. In particular, the 2DGFD method achieves 98.0% face recognition accuracy when using 20%3 feature matrices for each Gabor output on the ORL database and 97.6% recognition accuracy compared with 91.8% and 91.6% for the 2DPCA and 2DFLD method on the extended Yale database. The results show that the proposed 2DGFD method is computationally more efficient than the Gabor Fisher classifier method by approximately 8 times on the ORL, 135 times on the Yale and 1.2801%10 8 times on the extended Yale B data sets.

MutualBoost learning for selecting Gabor features for face recognition

This paper describes an improved boosting algorithm, the MutualBoost algorithm, and its application in developing a fast and robust Gabor feature based face recognition system. The algorithm uses mutual information to eliminate redundancy among Gabor features selected using the AdaBoost algorithm. Selected Gabor features are then subjected to Generalized Discriminant Analysis (GDA) for class separability enhancement before being used for face recognition. Compared with one of the top performers in the 2004 face verification competition, our method demonstrates clear advantages in classification accuracy, memory and computation. The method has been tested on the whole FERET database using the FERET evaluation protocol. Significant improvement in performance is observed. For example, existing Gabor based methods use a huge number of Gabor features, our method needs only hundreds of Gabor features to achieve very high classification accuracy. Due to substantially reduced feature dimension, memory and computation costs are reduced significantly – only 4 s are needed to recognize 200 face images.

Optimal sampling of Gabor features for face recognition

The Gabor feature is effective for facial image representation, while linear discriminant analysis (LDA) can extract the most discriminant information from the Gabor feature for face recognition. In practice, the dimension of a Gabor feature vector is so high that the computation and memory requirements are prohibitively large. To reduce the dimension, one simple scheme is to extract the Gabor feature at sub-sampled positions, usually in a regular grid, in a face region. However, this scheme is not effective enough and degrades the recognition performance. In this paper, we propose a method to determine the optimal position for extracting the Gabor feature such that the number of feature points is as small as possible while the representation capability of the points is as high as possible. The sub-sampled positions of the feature points are determined by a mask generated from a set of training images by means of principal component analysis (PCA). With the feature vector of reduced dimension, a subspace LDA is applied for face recognition, i.e., PCA is first used to reduce the dimension of the Gabor feature vectors generated from the sub-sampled positions, and then a common LDA is applied. Experimental results show that the new sampling method is simple, and effective for both dimension reduction and image representation. The recognition rate based on our proposed scheme is also higher than that achieved using a regular sampling method in a face region.

Independent component analysis of gabor features for face recognition

We present an independent Gabor features (IGFs) method and its application to face recognition. The novelty of the IGF method comes from 1) the derivation of independent Gabor features in the feature extraction stage and 2) the development of an IGF features-based probabilistic reasoning model (PRM) classification method in the pattern recognition stage. In particular, the IGF method first derives a Gabor feature vector from a set of downsampled Gabor wavelet representations of face images, then reduces the dimensionality of the vector by means of principal component analysis, and finally defines the independent Gabor features based on the independent component analysis (ICA). The independence property of these Gabor features facilitates the application of the PRM method for classification. The rationale behind integrating the Gabor wavelets and the ICA is twofold. On the one hand, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can, thus, produce salient local features that are most suitable for face recognition. On the other hand, ICA would further reduce redundancy and represent independent features explicitly. These independent features are most useful for subsequent pattern discrimination and associative recall. Experiments on face recognition using the FacE REcognition Technology (FERET) and the ORL datasets, where the images vary in illumination, expression, pose, and scale, show the feasibility of the IGF method. In particular, the IGF method achieves 98.5% correct face recognition accuracy when using 180 features for the FERET dataset, and 100% accuracy for the ORL dataset using 88 features.