Log-Gabor Wavelet Research Articles

Optimal Features Subset Selection and Classification for Iris Recognition

The selection of the optimal features subset and the classification have become an important issue in the field of iris recognition. We propose a feature selection scheme based on the multiobjectives genetic algorithm (MOGA) to improve the recognition accuracy and asymmetrical support vector machine for the classification of iris patterns. We also suggest a segmentation scheme based on the collarette area localization. The deterministic feature sequence is extracted from the iris images using the 1D log-Gabor wavelet technique, and the extracted feature sequence is used to train the support vector machine (SVM). The MOGA is applied to optimize the features sequence and to increase the overall performance based on the matching accuracy of the SVM. The parameters of SVM are optimized to improve the overall generalization performance, and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes. Our experimental results indicate that the performance of SVM as a classifier is better than the performance of the classifiers based on the feedforward neural network, the k-nearest neighbor, and the Hamming and the Mahalanobis distances. The proposed technique is computationally effective with recognition rates of 99.81% and 96.43% on CASIA and ICE datasets, respectively.

Multiscale Corner Detection of Gray Level Images Based on Log-Gabor Wavelet Transform

This paper presents two novel corner detection methods for gray level images based on log-Gabor wavelet transform. The input image is decomposed at multiscales and along multi-orientations. In the first algorithm, the magnitude along the direction that is orthogonal to the gradient orientation represents the cornerness measurement. Using this detector, corners are detected and localized accurately. Meanwhile, this method provides us the magnitudes and orientations along the principal axes corresponding to the directions of the local eigenvectors of the local changes. This information is useful when we deal with the affine transform applications in matching problems. The second proposed method is based on log-Gabor wavelets and second moment matrix. The input image is decomposed by the log-Gabor wavelets at multiscales along multi-orientations. Then the components at different scales and orientations are projected onto the x and y axis and formulated into the second moment matrix. Finally, the smaller eigenvalue of the second moment matrix is used to detect corner points. Compared with the most famous Harris detector and the recently published Kovesi's detector, the proposed methods provide better performance while retaining the good characteristics, such as robustness to noise, isotropic responses etc. Furthermore, the proposed methods address three problems existing in Harris detector. First, Harris method is based on single scale information. The proposed methods are based on the multiscales instead of single scale. Second, the derealization problem existing in Harris detector is minimized by utilizing good localization property of log-Gabor wavelets. Third, Harris detector has certain problems to detect corners of higher order structures. Due to the multi-orientation decomposition of the proposed methods, higher order structures are well computed. Consequently, the proposed methods detect corners of higher order structures very well. The proposed methods are first compared with Harris method and Kovesi's method in a subjective way. Then a classical stereo matching system is adopted to evaluate the corner detectors in an objective way. Both of the evaluations demonstrate better performance of the proposed methods.

Mean shift based log-Gabor wavelet image coding

In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the necessary wavelet coefficients with a mean shift based algorithm, and concentrates energy on the selected coefficients. It can sparsely approximate the original image, and converges faster than the existing local competition based method. Then, we propose a new compression scheme based on the above approximation method. The scheme has compression performance similar to JPEG 2000. The images decoded with the proposed compression scheme appear more pleasant to the human eyes than those with JPEG 2000.

Self-Invertible 2D Log-Gabor Wavelets

Orthogonal and biorthogonal wavelets became very popular image processing tools but exhibit major drawbacks, namely a poor resolution in orientation and the lack of translation invariance due to aliasing between subbands. Alternative multiresolution transforms which specifically solve these drawbacks have been proposed. These transforms are generally overcomplete and consequently offer large degrees of freedom in their design. At the same time their optimization gets a challenging task. We propose here the construction of log-Gabor wavelet transforms which allow exact reconstruction and strengthen the excellent mathematical properties of the Gabor filters. Two major improvements on the previous Gabor wavelet schemes are proposed: first the highest frequency bands are covered by narrowly localized oriented filters. Secondly, the set of filters cover uniformly the Fourier domain including the highest and lowest frequencies and thus exact reconstruction is achieved using the same filters in both the direct and the inverse transforms (which means that the transform is self-invertible). The present transform not only achieves important mathematical properties, it also follows as much as possible the knowledge on the receptive field properties of the simple cells of the Primary Visual Cortex (V1) and on the statistics of natural images. Compared to the state of the art, the log-Gabor wavelets show excellent ability to segregate the image information (e.g. the contrast edges) from spatially incoherent Gaussian noise by hard thresholding, and then to represent image features through a reduced set of large magnitude coefficients. Such characteristics make the transform a promising tool for processing natural images.

Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas

Several drawbacks of critically sampled wavelets can be solved by overcomplete multiresolution transforms and sparse approximation algorithms. Facing the difficulty to optimize such nonorthogonal and nonlinear transforms, we implement a sparse approximation scheme inspired from the functional architecture of the primary visual cortex. The scheme models simple and complex cell receptive fields through log-Gabor wavelets. The model also incorporates inhibition and facilitation interactions between neighboring cells. Functionally these interactions allow to extract edges and ridges, providing an edge-based approximation of the visual information. The edge coefficients are shown sufficient for closely reconstructing the images, while contour representations by means of chains of edges reduce the information redundancy for approaching image compression. Additionally, the ability to segregate the edges from the noise is employed for image restoration.

INFRARED FACE RECOGNITION BASED ON LOG-GABOR WAVELETS

Despite the variety of approaches and tools studied, face recognition is not accurate or robust enough to be used in uncontrolled environments. Recently, infrared (IR) imagery of human faces is considered as a promising alternative to visible imagery. IR face recognition is a biometric which offers the security of fingerprints with the convenience of face recognition. However, IR has its own limitations. The presence of eyeglasses has more influence on IR than visible imagery. In this paper, a method based on Log-Gabor wavelets for IR face recognition is proposed. The method first derives a Log-Gabor feature vector from IR face image, then obtains the independent Log-Gabor features by using independent component analysis (ICA). Experimental results show that the proposed method works well, even in challenging situations.

Using phase information for symmetry detection

Symmetry exists widely in the real world and plays a remarkable role in perception problems. The existing symmetry detection algorithms are mainly based on luminance or gradient information. Upon analyzing the relationship between symmetry and phase, based on phase congruency, we propose a phase-based symmetry detection (PSD) algorithm. PSD is calculated based on log Gabor wavelet. The symmetric points of objects are obtained by inspecting the phase information. The feasibility analysis, phase-based symmetry detection definition, and rationality demonstrations established the theoretic foundation for this algorithm. The experiments show that this algorithm can be applied directly to original images without segmentation—it is invariant to rotation, luminance and contrast—and it can detect several types of symmetries at the same time and it is compared with several other methods.

Textural feature based face recognition for single training images

A novel face recognition algorithm using single training face image is proposed. The algorithm is based on textural features extracted using the 2D log Gabor wavelet. These features are encoded into a binary pattern to form a face template which is used for matching. Experimental results show that on the colour FERET database the accuracy of the proposed algorithm is higher than the local feature analysis (LFA) and correlation filter (CF) based face recognition algorithms even when the number of training images is reduced to one. In comparison with recent single training image based face recognition algorithms, the proposed 2D log Gabor wavelet based algorithm shows an improvement of more than 3% in accuracy.

Phase based feature detector consistent with human visual system characteristics

Based on phase information, a novel image feature detector––symmetry phase congruency (SPC) is introduced. It is consistent with the characteristics of human visual systems. SPC is calculated based on log Gabor wavelet. It uses the phase information and is invariant to image illumination and contrast. SPC can detect the features in Mach bands and in sinusoidal grating. This cannot be done simply by looking for local maxima in intensity gradient or by looking for local energy maxima. Compared with phase congruency (PC), the advantages of SPC are demonstrated.