Fusion of classifiers for illumination robust face recognition

In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently a number of approaches have been proposed to deal with such variability.The aim of this work is twofold: first a survey on the existing techniques proposed to obtain an illumination robust recognition is given, and then a new method, based on the fusion of different classifiers, is proposed. The experiments carried out on different face databases confirm the effectiveness of the approach.     

Combination of multiple classifiers using probabilistic dictionary and its application to postcode recognition

Combination of multiple classifiers is regarded as an effective strategy for achieving a practical system of handwritten character recognition. A great deal of research on the methods of combining multiple classifiers has been reported to improve the recognition performance of single characters. However, in a practical application, the recognition performance of a group of characters (such as a postcode or a word) is more significant and more crucial. With the motivation of optimizing the recognition performance of postcode rather than that of single characters, this paper presents an approach to combine multiple classifiers in such a way that the combination decision is carried out at the postcode level rather than at the single character level, in which a probabilistic postcode dictionary is utilized as well to improve the postcode recognition ability. It can be seen from the experimental results that the proposed approach markedly improves the postcode recognition performance and outperforms the commonly used methods of combining multiple classifiers at the single character level. Furthermore, the sorting performance of some particular bins with respect to the postcodes with low frequency of occurrence can be improved significantly at the same time.     

Newborn face recognition using deep convolutional neural network

Multimedia Tools and Applications - Development of expertise in Face Recognition has led researchers to apply its various techniques for newborn recognition as some of the problems such as...     

Comparison of the nearest feature classifiers for face recognition

This paper presents an experimental comparison of the nearest feature classifiers, using an approach based on binomial tests in order to evaluate their strengths and weaknesses. In addition, classification accuracies and the accuracy-dimensionality tradeoff have been considered as comparison criteria. We extend two of the nearest feature classifiers to label the query point by a majority vote of the samples. Comparisons were carried out for face recognition using ORL database. We apply the eigenface representation for feature extraction. Experimental results showed that even though the classification accuracy of k-NFP outperforms k-NFL in some dimensions, these rate differences do not have statistical significance.     

Discriminant waveletfaces and nearest feature classifiers for face recognition

Feature extraction, discriminant analysis, and classification rules are three crucial issues for face recognition. We present hybrid approaches to handle three issues together. For feature extraction, we apply the multiresolution wavelet transform to extract the waveletface. We also perform the linear discriminant analysis on waveletfaces to reinforce discriminant power. During classification, the nearest feature plane (NFP) and nearest feature space (NFS) classifiers are explored for robust decisions in presence of wide facial variations. Their relationships to conventional nearest neighbor and nearest feature line classifiers are demonstrated. In the experiments, the discriminant waveletface incorporated with the NFS classifier achieves the best face recognition performance.     

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.     

Spoken emotion recognition using hierarchical classifiers

The recognition of the emotional state of speakers is a multi-disciplinary research area that has received great interest over the last years. One of the most important goals is to improve the voice-based human–machine interactions. Several works on this domain use the prosodic features or the spectrum characteristics of speech signal, with neural networks, Gaussian mixtures and other standard classifiers. Usually, there is no acoustic interpretation of types of errors in the results. In this paper, the spectral characteristics of emotional signals are used in order to group emotions based on acoustic rather than psychological considerations. Standard classifiers based on Gaussian Mixture Models, Hidden Markov Models and Multilayer Perceptron are tested. These classifiers have been evaluated with different configurations and input features, in order to design a new hierarchical method for emotion classification. The proposed multiple feature hierarchical method for seven emotions, based on spectral and prosodic information, improves the performance over the standard classifiers and the fixed features.     

Online activity recognition using evolving classifiers

This paper describes the use of evolving classifiers for activity recognition from sensor readings in ambient assisted living environments. Recognizing the activities an elderly person who lives alone performs, and identifying potential problems from the detected activities is a very active topic of research. However, current approaches do not take into account the fact that the way an activity is performed by a person evolves over time and therefore activities are identified by mapping them to a static model. In this work we describe and evaluate an approach for online classifying based on Evolving Fuzzy Systems (EFS): activities are described by a model that evolves over time, according to the changes observed in the way an activity is performed. These classifiers have been evaluated on three datasets obtained from real home settings, achieving a good recognition performance, at a confidence interval of 95%, compared with well know probabilistic models in terms of F-Measure, but improving their performance in terms of online capabilities and ability to adapt to the evolving ways in which activities are carried out.     

Voxel-based 3D face reconstruction and its application to face recognition using sequential deep learning

Multimedia Tools and Applications - In this paper, a novel 3D face reconstruction technique is proposed along with a sequential deep learning-based framework for face recognition. It uses the...     

Bagging null space locality preserving discriminant classifiers for face recognition

In this paper, we propose a novel bagging null space locality preserving discriminant analysis (bagNLPDA) method for facial feature extraction and recognition. The bagNLPDA method first projects all the training samples into the range space of a so-called locality preserving total scatter matrix without losing any discriminative information. The projected training samples are then randomly sampled using bagging to generate a set of bootstrap replicates. Null space discriminant analysis is performed in each replicate and the results of them are combined using majority voting. As a result, the proposed method aggregates a set of complementary null space locality preserving discriminant classifiers. Experiments on FERET and PIE subsets demonstrate the effectiveness of bagNLPDA.     

结合DCT和WT的多分类器融合的光照人脸识别

为进一步提高各种光照条件下的人脸识别精度,提出了一种将光照补偿和光照不变特征提取相结合的人脸识别方法。算法先应用对数域DCT进行光照补偿;然后,用三次样条二进小波分解提取一个低频子图和三个对光照变化鲁棒的边缘细节子图;接着,用二维线性判别分析进行特征降维并构造四个分量分类器;最后,通过多分类器融合规则进行融合分类。该文算法在CAS-PEAL人脸库光照子集上的实验达到了83.91%的识别率,在YaleB人脸库上则实现了100%的识别率,实验结果证明了该文算法对光照变换具有较好的鲁棒性。     

Aggregation of classifiers based on image transformations in biometric face recognition

In this study, we investigate the use of collective knowledge of independent classifiers (experts) in the area of face recognition. We formulate a hypothesis and provide compelling experimental evidence behind it that different image transformations can offer unique discriminatory information useful for face classification. We show that such discriminatory information can be combined in order to increase classification rates over those being produced by individual classifiers. In particular, we focus on contrast enhancement realized by histogram equalization and edge detection carried out with the use of the Sobel operator. We construct feature spaces emerging from linear and nonlinear methods of dimensionality reduction, namely Eigenfaces, Fisherfaces, kernel-PCA, and Isomap. Aggregation of classifiers is accomplished by majority voting and a Bayesian product rule. Extensive experimentation is conducted using the well-known FERET and YALE datasets.     

Attacks on state-of-the-art face recognition using attentional adversarial attack generative network

With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition system through obfuscation attack (rejecting a genuine subject) or impersonation attack (matching to an impostor). In this paper, we introduce a novel GAN, Attentional Adversarial Attack Generative Network (A³GN), to generate adversarial examples that mislead the network to identify someone as the target person not misclassify inconspicuously. For capturing the geometric and context information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces a face recognition network as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person.

     

An improved approach towards biometric face recognition using artificial neural network

In a biometric-based security system, rather than depending on the system configuration itself, failure rate also relies upon feature extraction and its related statistics. In this paper, a significant approach is being presented to minimize the failure rate and maintain high recognition accuracy and uniformity for non-symmetrical feature points. This work contributes a detailed analysis of stable parameters of captured biometric feature points by using a flexible learning model named as adopted Artificial Neural Network (ANN). The paper also discusses a comparative study of different global and local methods of Histogram of an Equivalent Pattern (HEP) technique for facial feature detection and extraction. The HEP, is further classified by using adopted ANN model, which depends on partitioning the feature area of a predefined image. This task has been accomplished by providing the appropriate definition of local and global functions based on pixel intensities. The literature available for face detection shows many shortcomings such as false acceptance and rejection rates. Among all defined global and local techniques, this paper primarily endorsed an adopted method of Improved Local Binary Pattern (ILBP) which works on local pixel values of a facial image for feature extraction. The classification and recognition task are performed by adopted ANN for various defined global and local features. The paper also derives a detailed comparison with the other existing techniques. As a result, the proposed ILBP technique ensures the consistency of acceptable results in unpredictable variations in the dataset.

     

LDA算法及其在人脸识别中的应用

线性特征提取在人脸识别中的应用非常广泛,LDA是其主要方法之一,它基于Fisher 判别准则,然而,当人脸训练样本数小于人脸样本向量的维数时,变换矩阵将无法直接得到,因此线性判别分析过程失效。采用了一种改进的基于Fisher 准则的LDA方法,针对小样本问题提出了一种有效地解决类内散布矩阵奇异的方法,而且用ORL人脸数据进行了实验验证。实验证明该方法在正确识别率方面表现突出。     

KIII模型及其在人脸识别中的应用

对一种模拟嗅觉神经系统的仿生模型、KIII模型、及其在人脸识别中的应用进行了研究。论文首先分析了KIII模型模式识别原理,然后基于人脸识别对模型的模式容量问题进行了研究。使用子图特征组合算法提取人脸特征后,在ORL人脸数据库上的实验结果表明,KIII模型的模式容量至少可以达到40,这也更新了之前认为KIII模型模式容量约为26的观点。     

Evolutionary pursuit and its application to face recognition

Introduces evolutionary pursuit (EP) as an adaptive representation method for image encoding and classification. In analogy to projection pursuit, EP seeks to learn an optimal basis for the dual purpose of data compression and pattern classification. It should increase the generalization ability of the learning machine as a result of seeking the trade-off between minimizing the empirical risk encountered during training and narrowing the confidence interval for reducing the guaranteed risk during testing. It therefore implements strategies characteristic of GA for searching the space of possible solutions to determine the optimal basis. It projects the original data into a lower dimensional whitened principal component analysis (PCA) space. Directed random rotations of the basis vectors in this space are searched by GA where evolution is driven by a fitness function defined by performance accuracy (empirical risk) and class separation (confidence interval). Accuracy indicates the extent to which learning has been successful, while separation gives an indication of expected fitness. The method has been tested on face recognition using a greedy search algorithm. To assess both accuracy and generalization capability, the data includes for each subject images acquired at different times or under different illumination conditions. EP has better recognition performance than PCA (eigenfaces) and better generalization abilities than the Fisher linear discriminant (Fisherfaces)     

改进人脸特征矫正网络的遮挡人脸识别方法

现有人脸识别模型受口罩等遮挡因素影响导致准确率无法提升。当前主流研究方法将有无遮挡场景分开训练后,整合应用于多场景。针对遮挡人脸识别模型的局限性,提出一种改进人脸特征矫正网络（FFR-Net）模型。该模型可同时用于有无遮挡人脸识别并应用于口罩与眼镜遮挡两种识别场景中。人脸特征矫正网络模型提出了一种人脸特征矫正模块,为保证充分利用无遮挡区域特征信息,在该模块中的空间分支引入involution算子扩大图像信息交互区域,增强在空间范围内面部特征信息;在通道分支引入坐标注意力机制,捕获跨通道信息以增强特征表示,利于模型准确地定位识别目标区域;将Meta-ACON作为该模块新的动态激活函数,通过动态调整线性或非线性程度以提高模型泛化能力和计算准确度。最后,利用改进的人脸特征矫正网络模型在CASIA-Webface经处理的有无口罩遮挡人脸数据集上进行训练,其在LFW经处理的有无口罩遮挡数据集、Meglass数据集上的测试结果准确率分别达到了82.50%和89.75%,优于现有算法,验证了所提方法的有效性。     

Methods for adaptive combination of classifiers with application to recognition of handwritten characters

This paper discusses two techniques for improving the recognition accuracy for online handwritten character recognition: committee classification and adaptation to the user. Combining classifiers is a common method for improving recognition performance. Improvements are possible because the member classifiers may make different errors. Much variation exists in handwritten characters, and adaptation is one feasible way of dealing with such variation. Even though adaptation is usually performed for single classifiers, it is also possible to use adaptive committees. Some novel adaptive committee structures, namely, the dynamically expanding context (DEC), modified current best learning (MCBL), and class-confidence critic combination (CCCC), are presented and evaluated. They are shown to be able to improve on their member classifiers, with CCCC offering the best performance. Also, the effect of having either more or less diverse sets of member classifiers is considered.Received: 17 September 2002, Accepted: 22 October 2002, Published online: 4 July 2003