多尺度非监督特征学习的人脸识别

为了充分利用人脸图像的潜在信息,提出一种通过设置不同尺寸的卷积核来得到图像多尺度特征的方法,多尺度卷积自动编码器（Multi-Scale Convolutional Auto-Encoder,MSCAE）。该结构所提取的不同尺度特征反映人脸的本质信息,可以更好地还原人脸图像。这种特征提取框架是一个卷积和采样交替的层级结构,使得特征对旋转、平移、比例缩放等具有高度不变性。MSCAE以encoder-decoder模式训练得到特征提取器,用它提取特征,并融合形成用于分类的特征向量。BP神经网络在ORL和Yale人脸库上的分类结果表明,多尺度特征在识别率和性能上均优于单尺度特征。此外,MSCAE特征与HOG（Histograms of Oriented Gradients）的融合特征取得了比单一特征更高的识别率。     

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.     

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.     

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.     

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。     

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。     

Decision boundary feature extraction for neural networks

In this paper, we propose a new feature extraction method for feedforward neural networks. The method is based on the recently published decision boundary feature extraction algorithm which is based on the fact that all the necessary features for classification can be extracted from the decision boundary. The decision boundary feature extraction algorithm can take advantage of characteristics of neural networks which can solve complex problems with arbitrary decision boundaries without assuming underlying probability distribution functions of the data. To apply the decision boundary feature extraction method, we first give a specific definition for the decision boundary in a neural network. Then, we propose a procedure for extracting all the necessary features for classification from the decision boundary. Experiments show promising results.     

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.     

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.     

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。     

Pedestrian detection with unsupervised multispectral feature learning using deep neural networks

Multispectral pedestrian detection is an important functionality in various computer vision applications such as robot sensing, security surveillance, and autonomous driving. In this paper, our motivation is to automatically adapt a generic pedestrian detector trained in a visible source domain to a new multispectral target domain without any manual annotation efforts. For this purpose, we present an auto-annotation framework to iteratively label pedestrian instances in visible and thermal channels by leveraging the complementary information of multispectral data. A distinct target is temporally tracked through image sequences to generate more confident labels. The predicted pedestrians in two individual channels are merged through a label fusion scheme to generate multispectral pedestrian annotations. The obtained annotations are then fed to a two-stream region proposal network (TS-RPN) to learn the multispectral features on both visible and thermal images for robust pedestrian detection. Experimental results on KAIST multispectral dataset show that our proposed unsupervised approach using auto-annotated training data can achieve performance comparable to state-of-the-art deep neural networks (DNNs) based pedestrian detectors trained using manual labels.     

Artificial neural networks for feature extraction and multivariatedata projection

Classical feature extraction and data projection methods have been well studied in the pattern recognition and exploratory data analysis literature. We propose a number of networks and learning algorithms which provide new or alternative tools for feature extraction and data projection. These networks include a network (SAMANN) for J.W. Sammon's (1969) nonlinear projection, a linear discriminant analysis (LDA) network, a nonlinear discriminant analysis (NDA) network, and a network for nonlinear projection (NP-SOM) based on Kohonen's self-organizing map. A common attribute of these networks is that they all employ adaptive learning algorithms which makes them suitable in some environments where the distribution of patterns in feature space changes with respect to time. The availability of these networks also facilitates hardware implementation of well-known classical feature extraction and projection approaches. Moreover, the SAMANN network offers the generalization ability of projecting new data, which is not present in the original Sammon's projection algorithm; the NDA method and NP-SOM network provide new powerful approaches for visualizing high dimensional data. We evaluate five representative neural networks for feature extraction and data projection based on a visual judgement of the two-dimensional projection maps and three quantitative criteria on eight data sets with various properties.     

Pattern fusion in feature recognition neural networks forhandwritten character recognition

B. Hussain and M.R. Kabuka (1994) proposed a feature recognition neural network to reduce the network size of neocognitron. However, a distinct subnet is created for every training pattern. Therefore, a big network is obtained when the number of training patterns is large. Furthermore, recognition rate can be hurt due to the failure of combining features from similar training patterns. We propose an improvement by incorporating the idea of fuzzy ARTMAP in the feature recognition neural network. Training patterns are allowed to be merged, based on the measure of similarity among features, resulting in a subnet being shared by similar patterns. Because of the fusion of training patterns, network size is reduced and recognition rate is increased.     

Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition

Recognizing human faces in various lighting conditions is quite a difficult problem. The problem becomes more difficult when face images are taken in extremely high dynamic range scenes. Most of the automatic face recognition systems assume that images are taken under well-controlled illumination. The face segmentation as well as recognition becomes much simpler under such a constrained condition. However, illumination control is not feasible when a surveillance system is installed in any location at will. Without compensating for uneven illumination, it is impossible to get a satisfactory recognition rate. In this paper, we propose an integrated system that first compensates uneven illumination through local contrast enhancement. Then the enhanced images are fed into a robust face recognition system which adaptively selects the most important features among all candidate features and performs classification by support vector machines (SVMs). The dimension of feature space as well as the selected types of features is customized for each hyperplane. Three face image databases, namely Yale, Yale Group B, and Extended Yale Group B, are used to evaluate performance. The experimental result shows that the proposed recognition system give superior results compared to recently published literatures.     

A cascade face recognition system using hybrid feature extraction

A novel cascade face recognition system using hybrid feature extraction is proposed. Three sets of face features are extracted. The merits of Two-Dimensional Complex Wavelet Transform (2D-CWT) are analyzed. For face recognition feature extraction, it has proved that 2D-CWT compares favorably with the traditionally used 2D Gabor transform in terms of the computational complexity and features? stability. The proposed recognition system congregates three Artificial Neural Network classifiers (ANNs) and a gating network trained by the three feature sets. A computationally efficient fitness function of the genetic algorithms is proposed to evolve the best weights of the ensemble classifier. Experiments demonstrated that the overall recognition rate and reliability have been significantly improved in both still face recognition and video-based face recognition.     

Evaluation of convolutional neural networks for visual recognition

Convolutional neural networks provide an efficient method to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. This network topology has been applied in particular to image classification when sophisticated preprocessing is to be avoided and raw images are to be classified directly. In this paper two variations of convolutional networks-neocognitron and a modification of neocognitron-are compared with classifiers based on fully connected feedforward layers with respect to their visual recognition performance. For a quantitative experimental comparison with standard classifiers two very different recognition tasks have been-chosen: handwritten digit recognition and face recognition. In the first example, the generalization of convolutional networks is compared to fully connected networks; in the second example human face recognition is investigated under constrained and variable conditions, and the limitations of convolutional networks are discussed.     

Pattern recognition properties of various feature spaces for higherorder neural networks

The authors explore alternatives that reduce the number of network weights while maintaining geometric invariant properties for recognizing patterns in real-time processing applications. This study is limited to translation and rotation invariance. The primary interest is in examining the properties of various feature spaces for higher-order neural networks (HONNs), in correlated and uncorrelated noise, such as the effect of various types of input features, feature size and number of feature pixels, and effect of scene size. The robustness of HONN training is considered in terms of target detectability. The experimental setup consists of a 15×20 pixel scene possibly containing a 3×10 target. Each trial used 500 training scenes plus 500 testing scenes. Results indicate that HONNs yield similar geometric invariant target recognition properties to classical template matching. However, the HONNs require an order of magnitude less computer processing time compared with template matching. Results also indicate that HONNs could be considered for real-time target recognition applications     

PCAPooL: unsupervised feature learning for face recognition using PCA,LBP, and pyramid pooling

Pattern Analysis and Applications - Human face is a widely used biometric modality for verification and revealing the identity of a person. In spite of a great deal of research on face recognition,...     

Complete discriminant evaluation and feature extraction in kernel space for face recognition

This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show that our approach consistently outperforms other kernel based face recognition methods.