SVM-based feature extraction for face recognition

The primary goal of linear discriminant analysis (LDA) in face feature extraction is to find an effective subspace for identity discrimination. The introduction of kernel trick has extended the LDA to nonlinear decision hypersurface. However, there remained inherent limitations for the nonlinear LDA to deal with physical applications under complex environmental factors. These limitations include the use of a common covariance function among each class, and the limited dimensionality inherent to the definition of the between-class scatter. Since these problems are inherently caused by the definition of the Fisher's criterion itself, they may not be solvable under the conventional LDA framework. This paper proposes to adopt a margin-based between-class scatter and a regularization process to resolve the issue. Essentially, we redesign the between-class scatter matrix based on the SVM margins to facilitate an effective and reliable feature extraction. This is followed by a regularization of the within-class scatter matrix. Extensive empirical experiments are performed to compare the proposed method with several other variants of the LDA method using the FERET, AR, and CMU-PIE databases.     

Arabic isolated word recognition system using hybrid feature extraction techniques and neural network

In this paper, we implemented a speaker-dependent speech recognition system for 11 standard Arabic isolated words. During the feature extraction phase, several techniques were used such as Mel frequency cepstral coefficients, perceptual linear prediction, relative perceptual linear prediction and their first order temporal derivatives. Principal component analysis was adopted in order to reduce the feature dimension. The recognition phase is based on the feed forward back-propagation neural network using two learning algorithms: the Levenberg–Marquardt “Trainlm” and the scaled conjugate gradient “Trainscg”. Hybrid approaches were used and compared in terms of computational time and recognition rates and have produced very interesting performances.     

Towards collaborative feature extraction for face recognition

Principal components analysis has become a popular preprocessing method to avoid the small sample size problem for most of the supervised graph embedding methods. Nevertheless, there is potential loss of relevant information when projecting the data onto the space defined by the principal Eigenfaces when the number of individuals in the gallery is large. This paper introduces a new collaborative feature extraction method based on projection pursuit, as a robust preprocessing for supervised embedding methods. A previously proposed projection index was adopted as a measure of interestingness, based on a weighted sum of six state of the art indices. We compare our collaborative feature extraction technique against principal component analysis as preprocessing stage for Laplacianfaces. For completeness, results for Eigenfaces and Fisherfaces are included. Experimental results to demonstrate the robustness of our approach against changes in facial expression and lighting are presented.     

Example image-based feature extraction for face recognition

This paper proposes a novel method for recognizing facial images based on the relative distances between an input image and example images. Example facial images can be easily collected online, and a large example database can span new possible facial variations not sufficiently learned during the learning phase. We first extract facial features using a baseline classifier that has a certain degree of accuracy. To achieve a better performance of the proposed method, we divide the collected examples into groups using a clustering method (e.g., k-means), where each clustered group contains examples with similar characteristics. We then hierarchically partition a group formed in the previous level into other groups to analyze more specific facial characteristics, which represent an example pyramid. To describe the characteristics of a group using the clustered examples, we divide the example group into a number of sub-groups. We calculate the averages of the sub-groups and select an example most similar to the average in each sub-group because we assume that the averages of the sub-groups can directly represent their characteristics. Using the selected examples, we build example code words for a novel feature extraction. The example code words are used to measure the distances to an input image and serve as anchors to analyze a facial image in the example domain. The distance values are normalized for each group at all pyramid levels, and are concatenated to form novel features for face recognition. We verified the effectiveness of the proposed example pyramid framework using well-known proposed features, including LBP, HOG, Gabor, and the deep learning method, on the LFW database, and showed that it can yield significant improvements in recognition performance.     

新的非线性鉴别特征抽取方法及人脸识别

在非线性空间中采用新的最大散度差鉴别准则,提出了一种新的核最大散度差鉴别分析方法.该方法不仅有效地抽取了人脸图像的非线性鉴别特征,而且从根本上避免了以往核Fisher鉴别分析中训练样本总数较多时,通常存在的核散布矩阵奇异的问题,计算复杂度大大降低,识别速度有了明显的提高.在ORL人脸数据库上的实验结果验证了该算法的有效性.     

自适应特征提取的光照鲁棒性人脸识别

针对光照对人脸特征提取的影响,提出了一种基于多尺度Curvelet变换的自适应局部熵的光照鲁棒性人脸特征提取方法。采用特殊局部对比增强算法对光照不均衡图像进行光照补偿,同时使图像局部特征显著;通过对增强后的图像进行Curvelet多尺度分解,得到的分解系数进行分块求熵从而构成候选特征向量;通过特征鉴别能力分析和评估,对候选特征值进行最优选择。在ORL,Yale,YaleB,AR四个人脸数据库中的实验结果表明,该方法与传统的PCA,LDA方法相比,避免小样本和特征分解问题,同时具有环境适应性和抗光照影响的特点。     

基于KIOFD算法的特征抽取及其在人脸识别中的应用

提出了一种基于核技术的融合了反转Fisher鉴别准则和正交化技术的KIOFD（Kernel Inverse Orthogonalized Fisher Discriminant）算法,并把这一算法应用于人脸识别中。线性人脸识别中存在两个突出问题:（1）在光照、表情、姿态变化较大时,人脸图像分类是复杂的、非线性的;（2）小样本问题,即当训练样本数量小于样本特征空间维数时,导致类内散布矩阵奇异。对于第1个问题,可以采用核技术提取人脸图像样本的非线性特征,对于第2个问题,采用了反转Fisher鉴别准则和正交化结合的算法。通过对ORL、Yale Group B以及UMIST3个人脸库的实验表明,提出的算法是可行的、高效的。     

面向人脸识别的WPD-HOG金字塔特征提取方法

人脸识别技术可应用于各监控和安保领域,它涉及特征提取、识别模型等关键技术。其中特征提取方法直接影响识别效果,目前所用的特征提取方法存在特征表达不全面、计算复杂度高等问题。据此,提出一种基于WPD-HOG金字塔的人脸特征提取方法,该方法结合小波包分解（Wavelet Packet Decomposition,WPD）、图像金字塔以及方向梯度直方图（Histograms of Oriented Gradients,HOG）对人脸图像特征进行有效表征,最终将WPD-HOG金字塔特征通过SVM分类器进行分类。通过在ORL人脸库上进行实验,与四种对比方法HOG、HOG金字塔、FWPD-HOG以及FWPD-HOG金字塔进行比较,实验结果表明,WPD-HOG金字塔特征提取方法的识别率要高于对比方法,且在噪声方面具有较好的鲁棒性。     

A novel face recognition system using hybrid neural and dual eigenspaces methods

In this paper, we present an automated face recognition (AFR) system that contains two components: eye detection and face recognition. Based on invariant radial basis function (IRBF) networks and knowledge rules of facial topology, a hybrid neural method is proposed to localize human eyes and segment the face region from a scene. A dual eigenspaces method (DEM) is then developed to extract algebraic features of the face and perform the recognition task with a two-layer minimum distance classifier. Experimental results illustrate that the proposed system is effective and robust.     

Accurate computing of facial expression recognition using a hybrid feature extraction technique

The Journal of Supercomputing - Facial expression recognition (FER) serves as an essential tool for understanding human emotional behaviors. Facial expressions provide a wealth of information about...     

Human facial feature extraction for face interpretation and recognition

Facial features' extraction algorithms which can be used for automated visual interpretation and recognition of human faces are presented. Here, we can capture the contours of the eye and mouth by a deformable template model because of their analytically describable shapes. However, the shapes of the eyebrow, nostril and face are difficult to model using a deformable template. We extract them by using an active contour model (snake). In the experiments, 12 models are photographed, and the feature contours are extracted for each portrait.     

Shared feature extraction for nearest neighbor face recognition.

In this paper, we propose a new supervised linear feature extraction technique for multiclass classification problems that is specially suited to the nearest neighbor classifier (NN). The problem of finding the optimal linear projection matrix is defined as a classification problem and the Adaboost algorithm is used to compute it in an iterative way. This strategy allows the introduction of a multitask learning (MTL) criterion in the method and results in a solution that makes no assumptions about the data distribution and that is specially appropriated to solve the small sample size problem. The performance of the method is illustrated by an application to the face recognition problem. The experiments show that the representation obtained following the multitask approach improves the classic feature extraction algorithms when using the NN classifier, especially when we have a few examples from each class.     

A novel fuzzy facial expression recognition system based on facial feature extraction from color face images

Emotion recognition plays an effective and important role in Human-Computer Interaction (HCI). Recently, various approaches to emotion recognition have been proposed in the literature, but they do not provide a powerful approach to recognize emotions from Partially Occluded Facial Images.In this paper, we propose a new method for Emotion Recognition from Facial Expression using Fuzzy Inference System (FIS). This novel method is even able to recognize emotions from Partially Occluded Facial Images. Moreover, this research describes new algorithms for facial feature extraction that demonstrate satisfactory performance and precision. In addition, one of the main factors that have an important influence on the final precision of fuzzy inference systems is the membership function parameters. Therefore, we use a Genetic Algorithm for parameter-tuning of the membership functions. Experimental results report an average precision rate of 93.96% for Emotion Recognition of six basic emotions, which is so promising.     

A face and palmprint recognition approach based on discriminant DCT feature extraction

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.     

Pattern recognition using feature feedback: Application to face recognition

In this paper, we propose a new pattern recognition method using feature feedback and present its application to face recognition. Conventional pattern recognition methods extract the features employed for classification using PCA, LDA and so on. On the other hand, in the proposed method, the extracted features are analyzed in the original space using feature feedback. Using reverse mapping from the extracted features to the original space, we can identify the important part of the original data that affects the classification. In this way, we can modify the data to obtain a higher classification rate, make it more compact or abbreviate the required sensors. To verify the applicability of the proposed method, we apply it to face recognition using the Yale Face Database. Each face image is divided into two parts, the important part and unimportant part, using feature feedback, and the classification performed using the feature mask obtained from feature feedback. Also, we combine face recognition with image compression. The experimental results show that the proposed method works well.     

Evaluation of unsupervised feature extraction neural networks for face recognition

In this paper, new appearances based on neural networks (NN) algorithms are presented for face recognition. Face recognition is subdivided into two main stages: feature extraction and classifier. The suggested NN algorithms are the unsupervised Sanger principal component neural network (Sanger PCNN) and the self-organizing feature map (SOFM), which will be applied for features extraction of the frontal view of a face image. It is of interest to compare the unsupervised network with the traditional Eigenfaces technique. This paper presents an experimental comparison of the statistical Eigenfaces method for feature extraction and the unsupervised neural networks in order to evaluate the classification accuracies as comparison criteria. The classifier is done by the multilayer perceptron (MLP) neural network. Overcoming of the problem of the finite number of training samples per person is discussed. Experimental results are implemented on the Olivetti Research Laboratory database that contains variability in expression, pose, and facial details. The results show that the proposed method SOFM/MLP neural network is more efficient and robust than the Sanger PCNN/MLP and the Eigenfaces/MLP, when used a few number of training samples per person. As a result, it would be more applicable to utilize the SOFM/MLP NN in order to accomplish a higher level of accuracy within a recognition system.     

自适应Gabor特征的协同表示人脸识别算法

为了解决人脸识别应用中针对人脸姿态的变化,光照等外部环境变化导致识别率不高,且稀疏表示应用于人脸识别收敛速度慢的情况,提出了一种基于多分量的Gabor特征提取和自适应权重选择的协同表示人脸识别算法(GAW-CRC).特征提取阶段,将Gabor变换的所有特征分量中鉴别能力较差的分量淘汰,剩余分量构建特征字典,分别协同表示对应测试样本的特征分量,将所有剩余分量的识别结果,按照自适应的权重函数加权融合得出最终分类结果.实验证明:算法应用于AR,FERET与Extended Yale B人脸库中,当对应的样本存在人脸角度变化,表情变化和光照条件变化等情况时,能够得到更高的识别率.     

A robust approach based on local feature extraction for age invariant face recognition

Multimedia Tools and Applications - Age variation is a major problem in the area of face recognition under uncontrolled environment such as pose, illumination, expression. Most of the works of this...     

A fuzzy target recognition system with homomorphic invariant feature extraction

A target recognition system based on the concept of fuzzy set theory and homomorphic system is proposed. It involves automatic threshold selection, feature extraction, and classification. An optimal threshold is selected by the fuzzy risk criterion, i.e. to separate a given image into meaningful grey level classes under the assumption that the object and pixel grey level values are normally distributed. An edge measure for evaluating thresholding methods is also presented. When an image is segmented, a set of invariant features called mean, variance, skewness, and kurtosis, which are derived from the spectrum histogram of the target image, is calculated. The classification is then accomplished using the membership function of the feature space of an image and stored patterns. By simulation results, we find that the fuzzy risk thresholding achieves significant performance according to the uniformity and edge measures, and the fuzzy filter with the invariant features has good performance even in low signal-to-noise ratio conditions.