A Bayesian discriminating features method for face detection

This paper presents a novel Bayesian discriminating features (BDF) method for multiple frontal face detection. The BDF method, which is trained on images from only one database, yet works on test images from diverse sources, displays robust generalization performance. The novelty of this paper comes from the integration of the discriminating feature analysis of the input image, the statistical modeling of face and nonface classes, and the Bayes classifier for multiple frontal face detection. First, feature analysis derives a discriminating feature vector by combining the input image, its 1D Harr wavelet representation, and its amplitude projections. While the Harr wavelets produce an effective representation for object detection, the amplitude projections capture the vertical symmetric distributions and the horizontal characteristics of human face images. Second, statistical modeling estimates the conditional probability density functions, or PDFs, of the face and nonface classes, respectively. While the face class is usually modeled as a multivariate normal distribution, the nonface class is much more difficult to model due to the fact that it includes "the rest of the world." The estimation of such a broad category is, in practice, intractable. However, one can still derive a subset of the nonfaces that lie closest to the face class, and then model this particular subset as a multivariate normal distribution.     

Feature extraction and dimensionality reduction by genetic programming based on the Fisher criterion

Abstract: Feature extraction helps to maximize the useful information within a feature vector, by reducing the dimensionality and making the classification effective and simple. In this paper, a novel feature extraction method is proposed: genetic programming (GP) is used to discover features, while the Fisher criterion is employed to assign fitness values. This produces non‐linear features for both two‐class and multiclass recognition, reflecting the discriminating information between classes. Compared with other GP‐based methods which need to generate c discriminant functions for solving c‐class (c>2) pattern recognition problems, only one single feature, obtained by a single GP run, appears to be highly satisfactory in this approach. The proposed method is experimentally compared with some non‐linear feature extraction methods, such as kernel generalized discriminant analysis and kernel principal component analysis. Results demonstrate the capability of the proposed approach to transform information from the high‐dimensional feature space into a single‐dimensional space by automatically discovering the relationships between data, producing improved performance.     

Graph-based recognition of morphological features

Automatic recognition of the shapes of objects represented as solid models is very important in design optimization. Object shape also governs ease of manufacture, ease of orientability, field use and all other life-cycle applications. Characteristic attributes of an object shape such as chamfers, protrusions and depressions, play a significant role in process planning, design for manufacture, etc. These attributes are popularly known as morphological features. In this paper, the problem of identifying such morphological features is divided into two phases: the feature extraction phase and the feature classification phase. In feature extraction, the mechanical part is decomposed into its constituent features such as holes, protrusions and depressions based on the connectivity class in the edge-face graph of the part. In feature classification, on the other hand, the extracted features are identified and classified. This paper describes a feature classification scheme based on topological and geometric attributes of a morphological feature. The feature classification scheme outlined in this paper is capable of identifying new features with minimal human interface.     

基于多特征融合和BoostingRBF神经网络的人脸识别

提出一种多特征信息融合的人脸识别方法.应用Zernike矩方法和非负矩阵分解法(NMF)分别提取具有旋转不变性的人脸几何特征和人脸子空间投影系数特征,将这两种具有一定互补性的特征串行融合,得到一个分类能力更强的特征.在此基础上,采用RBF神经网络进行人脸识别.为了提高神经网络的分类准确率和泛化能力,采用Boosting方法进行网络集成.实验结果表明,提出的算法利用较少样本数据即可快速地进行人脸识别.     

Unsupervised feature selection applied to content-based retrieval of lung images

This paper describes a new hierarchical approach to content-based image retrieval called the "customized-queries" approach (CQA). Contrary to the single feature vector approach which tries to classify the query and retrieve similar images in one step, CQA uses multiple feature sets and a two-step approach to retrieval. The first step classifies the query according to the class labels of the images using the features that best discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features customized to distinguish "subclasses" within that class. Needing to find the customized feature subset for each class led us to investigate feature selection for unsupervised learning. As a result, we developed a new algorithm called FSSEM (feature subset selection using expectation-maximization clustering). We applied our approach to a database of high resolution computed tomography lung images and show that CQA radically improves the retrieval precision over the single feature vector approach. To determine whether our CBIR system is helpful to physicians, we conducted an evaluation trial with eight radiologists. The results show that our system using CQA retrieval doubled the doctors' diagnostic accuracy.     

Feature fusion of side face and gait for video-based human identification

Video-based human recognition at a distance remains a challenging problem for the fusion of multimodal biometrics. As compared to the approach based on match score level fusion, in this paper, we present a new approach that utilizes and integrates information from side face and gait at the feature level. The features of face and gait are obtained separately using principal component analysis (PCA) from enhanced side face image (ESFI) and gait energy image (GEI), respectively. Multiple discriminant analysis (MDA) is employed on the concatenated features of face and gait to obtain discriminating synthetic features. This process allows the generation of better features and reduces the curse of dimensionality. The proposed scheme is tested using two comparative data sets to show the effect of changing clothes and face changing over time. Moreover, the proposed feature level fusion is compared with the match score level fusion and another feature level fusion scheme. The experimental results demonstrate that the synthetic features, encoding both side face and gait information, carry more discriminating power than the individual biometrics features, and the proposed feature level fusion scheme outperforms the match score level and another feature level fusion scheme. The performance of different fusion schemes is also shown as cumulative match characteristic (CMC) curves. They further demonstrate the strength of the proposed fusion scheme.     

Speech recognition with unknown partial feature corruption – a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.     

Feature combination for binary pattern classification

The paper presents a novel framework for large class, binary pattern classification problem by learning-based combination of multiple features. In particular, class of binary patterns including characters/primitives and symbols has been considered in the scope of this work. We demonstrate novel binary multiple kernel learning-based classification architecture for applications including such problems for fast and efficient performance. The character/primitive classification problem primarily concentrates on Gujarati and Bangla character recognition from the analytical and experimental context. A novel feature representation scheme for symbols images is introduced containing the necessary elastic and non-elastic deformation invariance properties. The experimental efficacy of proposed framework for symbol classification has been demonstrated on two public data sets.     

基于组合特征和BTSVM的电能质量扰动识别

为了克服单一特征不能完全表征各种暂态扰动信号特征的不足,提出了一种基于组合特征和二叉树结构支持向量机相结合的电能质量多分类方案。利用小波包变换对扰动信号进行分解,提取特定频带下信号的能量,利用S变换获得扰动信号的模矩阵,从中提取出特征信息,然后将多频带信号的能量和对应的S变换特征信息组合得到组合特征。对依据聚类思想设计出的二叉树结构支持向量机分类器进行了训练和测试。仿真结果表明,该方法具有较好的准确性和识别速度,能够有效识别常见扰动信号,平均识别率提高了6%以上,测试总用时缩短0.06秒,训练时间减小1.8秒。     

Class-specific multiple classifiers scheme to recognize emotions from speech signals

Automatic emotion recognition from speech signals is one of the important research areas, which adds value to machine intelligence. Pitch, duration, energy and Mel-frequency cepstral coefficients (MFCC) are the widely used features in the field of speech emotion recognition. A single classifier or a combination of classifiers is used to recognize emotions from the input features. The present work investigates the performance of the features of Autoregressive (AR) parameters, which include gain and reflection coefficients, in addition to the traditional linear prediction coefficients (LPC), to recognize emotions from speech signals. The classification performance of the features of AR parameters is studied using discriminant, k-nearest neighbor (KNN), Gaussian mixture model (GMM), back propagation artificial neural network (ANN) and support vector machine (SVM) classifiers and we find that the features of reflection coefficients recognize emotions better than the LPC. To improve the emotion recognition accuracy, we propose a class-specific multiple classifiers scheme, which is designed by multiple parallel classifiers, each of which is optimized to a class. Each classifier for an emotional class is built by a feature identified from a pool of features and a classifier identified from a pool of classifiers that optimize the recognition of the particular emotion. The outputs of the classifiers are combined by a decision level fusion technique. The experimental results show that the proposed scheme improves the emotion recognition accuracy. Further improvement in recognition accuracy is obtained when the scheme is built by including MFCC features in the pool of features.     

Online Signature Verification and Recognition: An Approach Based on Symbolic Representation

In this paper, we propose a new method of representing on-line signatures by interval valued symbolic features. Global features of on-line signatures are used to form an interval valued feature vectors. Methods for signature verification and recognition based on the symbolic representation are also proposed. We exploit the notions of writer dependent threshold and introduce the concept of feature dependent threshold to achieve a significant reduction in equal error rate. Several experiments are conducted to demonstrate the ability of the proposed scheme in discriminating the genuine signatures from the forgeries. We investigate the feasibility of the proposed representation scheme for signature verification and also signature recognition using all 16500 signatures from 330 individuals of the MCYT bimodal biometric database. Further, extensive experimentations are conducted to evaluate the performance of the proposed methods by projecting features onto Eigenspace and Fisherspace. Unlike other existing signature verification methods, the proposed method is simple and efficient. The results of the experimentations reveal that the proposed scheme outperforms several other existing verification methods including the state-of-the-art method for signature verification.     

Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

In this paper, we present a scheme based on feature mining and pattern classification to detect LSB matching steganography in grayscale images, which is a very challenging problem in steganalysis. Five types of features are proposed. In comparison with other well-known feature sets, the set of proposed features performs the best. We compare different learning classifiers and deal with the issue of feature selection that is rarely mentioned in steganalysis. In our experiments, the combination of a dynamic evolving neural fuzzy inference system (DENFIS) with a feature selection of support vector machine recursive feature elimination (SVMRFE) achieves the best detection performance. Results also show that image complexity is an important reference to evaluation of steganalysis performance.     

In Defense of Active Part Selection for Fine-Grained Classification

Fine-grained classification is a recognition task where subtle differences distinguish between different classes. To tackle this classification problem, part-based classification methods are mostly used. Partbased methods learn an algorithm to detect parts of the observed object and extract local part features for the detected part regions. In this paper we show that not all extracted part features are always useful for the classification. Furthermore, given a part selection algorithm that actively selects parts for the classification we estimate the upper bound for the fine-grained recognition performance. This upper bound lies way above the current state-of-the-art recognition performances which shows the need for such an active part selection method. Though we do not present such an active part selection algorithm in this work, we propose a novel method that is required by active part selection and enables sequential part-based classification. This method uses a support vector machine (SVM) ensemble and allows to classify an image based on arbitrary number of part features. Additionally, the training time of our method does not increase with the amount of possible part features. This fact allows to extend the SVM ensemble with an active part selection component that operates on a large amount of part feature proposals without suffering from increasing training time.     

基于特征融合的人体行为识别算法

针对HOG特征在人体行为识别中仅仅表征人体局部梯度特征的不足,提出了一种扩展HOG（ExHOG）特征与CLBP特征相融合的人体行为识别方法。用背景差分法从视频中提取出完整的人体运动序列,并提取出扩展梯度方向直方图ExHOG及完备局部二值模式CLBP两种互补特征;利用K-L变换将这两种互补特征融合生成一个分类能力更强的行为特征;采用径向基函数神经网络RBFNN对行为特征进行识别分类。在KTH和Weizman行为公共数据库上进行了多组实验,结果表明提出的方法能够有效地识别人体运动类别。     

基于支持向量机的电路故障诊断模型

提出了一种基于遗传编程和支持向量机的故障诊断模型。通过遗传编程对时域指标进行特征选择和提取,得到更能反映信号本质的特征信号,该特征信号可作为识别特征输入多类支持向量机,实现对模拟电路不同类型软故障的识别。实验结果表明,同传统时域指标相比,经过遗传选择和提取的特征对模拟电路的软故障具有更好的识别能力,进而提高了多类支持向量机的分类准确性。     

基于对比学习的多特征融合戴口罩人脸识别

随着计算机视觉技术应用的发展和智能终端的普及,口罩遮挡人脸识别已成为人物身份信息识别的重要部分。口罩的大面积遮挡对人脸特征的学习带来极大挑战。针对戴口罩人脸特征学习困难这一问题,提出了一种基于对比学习的多特征融合口罩遮挡人脸识别算法,该算法改进了传统的基于三元组关系的人脸特征向量学习损失函数,提出了基于多实例关系的损失函数,充分挖掘戴口罩人脸和完整人脸多个正负样本之间的同模态内和跨模态间的关联关系,学习人脸中具有高区分度的能力的特征,同时结合人脸的眉眼等局部特征和轮廓等全局特征,学习口罩遮挡人脸的有效特征向量表示。在真实的戴口罩人脸数据集和生成的戴口罩人脸数据上与基准算法进行了比较,实验结果表明所提算法相比传统的基于三元组损失函数和特征融合算法具有更高的识别准确率。     

An improved feature transformation method using mutual information

The feature transformation is a very important step in pattern recognition systems. A feature transformation matrix can be obtained using different criteria such as discrimination between classes or feature independence or mutual information between features and classes. The obtained matrix can also be used for feature reduction. In this paper, we propose a new method for finding a feature transformation-based on Mutual Information (MI). For this purpose, we suppose that the Probability Density Function (PDF) of features in classes is Gaussian, and then we use the gradient ascent to maximize the mutual information between features and classes. Experimental results show that the proposed MI projection consistently outperforms other methods for a variety of cases. In the UCI Glass database we improve the classification accuracy up to 7.95 %. Besides, the improvement of phoneme recognition rate is 3.55 % on TIMIT.     

Partial discriminative training for classification of overlapping classes in document analysis

For character recognition in document analysis, some classes are closely overlapped but are not necessarily to be separated before contextual information is exploited. For classification of such overlapping classes, either discriminating between them or merging them into a metaclass does not satisfy. Merging the overlapping classes into a metaclass implies that within-metaclass substitution is considered as correct classification. For such classification problems, this paper proposes a partial discriminative training (PDT) scheme, in which, a training pattern of an overlapping class is used as a positive sample of its labeled class, and neither positive nor negative sample for its allied classes (those overlapping with the labeled class). In experiments of offline handwritten letter and online symbol recognition using various classifiers evaluated at metaclass level, the PDT scheme mostly outperforms ordinary discriminative training and merged metaclass classification. This work was supported by the Hundred Talents Program of Chinese Academy of Sciences and the National Natural Science Foundation of China (NSFC) under Grants No. 60775004 and No. 60723005.