Analysis of variance of Gabor filter banks parameters for optimal face recognition

Gabor filter banks constitute a very robust tool to extract discriminant information from a visual scene. After the now “classical” bank with 5 frequencies and 8 orientations proposed by Lades et al. and Wiskott et al., many other parametrizations of a Gabor filter bank have appeared. In order to find the optimal parametrization for a face recognition experiment, we have performed a 6-way analysis of variance of Gabor parameters using FERET, FRAV2D, FRAV3D, FRGC and XM2VTS face databases, including frontal and turned poses, facial expressions, occlusions and changes of illumination. Considering independent criteria to find the optimal Gabor filter bank, the bank with the highest recognition rate was found to have 6 frequencies and narrower Gaussian widths in the space domain. These results were obtained with Mahalanobis distance for a k-NN classifier, with analytical and holistic Gabor feature vectors. Moreover about 20% of the banks studied here obtained in average a better performance than the classical bank. For most of the databases considered, the highest recognition rates have been achieved with analytical representations (frontal images, images with turns or occlusions), with a holistic preponderance for images with gestures or changes of illumination. The inferiority found for holistic Gabor representations versus their analytical counterparts can be explained for the intrinsic redundancy and the size of the feature vectors of this kind of representation.     

Learning the best subset of local features for face recognition

We propose a novel, local feature-based face representation method based on two-stage subset selection where the first stage finds the informative regions and the second stage finds the discriminative features in those locations. The key motivation is to learn the most discriminative regions of a human face and the features in there for person identification, instead of assuming a priori any regions of saliency. We use the subset selection-based formulation and compare three variants of feature selection and genetic algorithms for this purpose. Experiments on frontal face images taken from the FERET dataset confirm the advantage of the proposed approach in terms of high accuracy and significantly reduced dimensionality.     

Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models

A novel face recognition algorithm based on Gabor texture information is proposed in this paper. Two kinds of strategies to capture it are introduced: Gabor magnitude-based texture representation (GMTR) and Gabor phase-based texture representation (GPTR). Specifically, GMTR is characterized by using the Gamma density (Γ$\Gamma$ D) to model the Gabor magnitude distribution, while GPTR is characterized by using the generalized Gaussian density (GGD) to model the Gabor phase distribution. The estimated model parameters serve as texture representation. Experiments are performed on Yale, ORL and FERET databases to validate the feasibility of the proposed method. The results show that the proposed GMTR-based and GPTR-based NLDA both significantly outperform the widely used Gabor features-based NLDA and other existing subspace methods. In addition, the feature level fusion of these two kinds of texture representations performs better than them individually.     

Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count

Face recognition has a wide range of possible applications in surveillance, human computer interfaces and marketing and advertising goods for selected customers according to age and gender. Because of the high classification rate and reduced computational time, one of the best methods for face recognition is based on Gabor jet feature extraction and Borda count classification. In this paper, we propose methodological improvements to increase face recognition rate by selection of Gabor jets using entropy and genetic algorithms. This selection of jets additionally allows faster processing for real-time face recognition. We also propose improvements in the Borda count classification through a weighted Borda count and a threshold to eliminate low score jets from the voting process to increase the face recognition rate. Combinations of Gabor jet selection and Borda count improvements are also proposed. We compare our results with those published in the literature to date and find significant improvements. Our best results on the FERET database are 99.8%, 99.5%, 89.2% and 86.8% recognition rates on the subsets Fb, Fc, Dup1 and Dup2, respectively. Compared to the best results published in the literature, the total number of recognition errors decreased from 163 to 112 (31%). We also tested the proposed method under illumination changes, occlusions with sunglasses and scarves and for small pose variations. Results on two different face databases (AR and Extended Yale B) with significant illumination changes showed over 90% recognition rate. The combination EJS-BTH-BIP reached 98% and 99% recognition rate in images with sunglasses and scarves from the AR database, respectively. The proposed method reached 93.5% recognition on faces with small pose variation of 25° rotation and 98.5% with 15% rotation in the FERET database.     

A face recognition system based on automatically determined facial fiducial points

In this paper, a completely automatic face recognition system is presented. The method works on color images: after having localized the face and the facial features, it determines 24 facial fiducial points, and characterizes them applying a bank of Gabor filters which extract the peculiar texture around them (jets). Recognition is realized measuring the similarity between the different jets. The system is inspired by the elastic bunch graph method, while it does no assumption on the scale, pose, and the background. Comparison with standard algorithms is presented and discussed.     

Simplified Gabor wavelets for human face recognition

Gabor wavelets (GWs) are commonly used for extracting local features for various applications such as object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGW features for face recognition. Experimental results show that using SGWs can achieve a performance level similar to using GWs, while the runtime for feature extraction using SGWs is, at most, 4.39 times faster than that of GWs implemented by using the fast Fourier transform (FFT).     

Texture classification using Gabor wavelets based rotation invariant features

Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multi-resolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.     

结合Haar与Gabor特征的Adaboost人脸识别改进算法

在模式识别的众多领域中,针对于人脸识别的研究与应用逐渐成为重点和难点。尽管近些年众多学者不断钻研并改进人脸识别的算法,但是在复杂光照条件下和不同肤色的人脸识别中仍然存在着重重的不足。尤其是由于现阶段数据的计算速度和存储条件仍然不能很好地去适应优秀的算法对其的要求,因此如何改进算法,提高人脸识别的精度是本文主要研究的问题。本文结合Haar与Gabor特征提出了Adaboost人脸识别算法的改进方法,提高了人脸识别的速度和精度。     

基于Gabor-ILLE的多姿态人耳识别

针对LLE算法对姿态变化和近邻点敏感的缺陷,提了一种融合Gabor小波和改进LLE算法的人耳识别算法（Gabor-ILLE）。该算法通过Gabor变换提取人耳特征,并对Gabor初始特征融合,采用改进LLE对特征进行降维,选择最有利于人耳识别的Gabor特征,采用K近邻算法建立人耳分类器实现人耳识别,并采用USTB3人耳图像库进行仿真实验。相对于参比人耳算法,Gabor-ILLE获得了更高的人耳识别率,实验结果验证了Gabor-ILLE算法的有效性。     

基于Gabor小波变换的人脸表情识别技术研究

为了使人机交互得到更好的研究,提出了一种基于Gabor小波变换的人脸表情识别的新方法;首先对图像进行预处理以提高后续处理的准确度,预处理包括确定纯的人脸表情区域,尺寸和灰度归一化,然后对表情子区域进行Gabor小波变换,提取表情特征矢量,进而构建表情弹性图,最后用模板匹配的方法来识别图像的人脸表情;经过实验,发现Gabor小波变换提取特征时受光照影响比较小,该系统不仅具有很好的鲁棒性,并且速度快,识别率高。