相对边缘方向幅值模式耦合SIFT的人脸识别算法

人脸识别易受到光照变化、遮挡等影响,降低了识别准确率,为此提出一种基于相对边缘方向幅值模式(relative patterns of oriented edge magnitudes,RPOEM)与尺度不变特征变换(scale-invariant feature transform,SIFT)的人脸识别算法.检测脸部的...     

局部方向梯度幅值与相位差分的人脸识别算法

针对传统人脸识别算法在单训练样本下效果不佳,提出一种局部方向梯度幅值和相位差分相结合的方法（LDGMPD）,首先提取图像的梯度幅值与相位,梯度幅值图像与8个Kirsch模板卷积得到每个子邻域的8个边缘梯度值;然后对相位进行局部差分。局部方向梯度幅值与相位差分仅使用边缘梯度值与相位局部差分值中最大值的方向编码成一个二位八进制数,产生LDGMPD值。再选取结构对比信息对各LDGMPD人脸分块进行加权处理,提取人脸的LDGMPD直方图特征,最后利用最近邻分类器分类识别。在AR和CAS-PEAL-R1共享库上进行实验表明LDGMPD在单样本人脸识别具有较好的效果。     

Face recognition for smart environments

Smart environments, wearable computers, and ubiquitous computing in general are the coming “fourth generation” of computing and information technology. But that technology will be a stillbirth without new interfaces for interaction, minus a keyboard or mouse. To win wide consumer acceptance, these interactions must be friendly and personalized; the next generation interfaces must recognize people in their immediate environment and, at a minimum, know who they are. In this article, the authors discuss face recognition technology, how it works, problems to be overcome, current technologies, and future developments and possible applications. Twenty years ago, the problem of face recognition was considered among the most difficult in artificial intelligence and computer vision. Today, however, there are several companies that sell commercial face recognition software that is capable of high-accuracy recognition with databases of more than 1,000 people. The authors describe the face recognition technology used, explaining the algorithms for face recognition as well as novel applications, such as behavior monitoring that assesses emotions based on facial expressions     

Face recognition: features versus templates

Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching, are presented. The results obtained for the testing sets show about 90% correct recognition using geometrical features and perfect recognition using template matching     

Face and gesture recognition: overview

Computerised recognition of faces and facial expressions would be useful for human-computer interface, and provision for facial animation is to be included in the ISO standard MPEG-4 by 1999. This could also be used for face image compression. The technology could be used for personal identification, and would be proof against fraud. Degrees of difference between people are discussed, with particular regard to identical twins. A particularly good feature for personal identification is the texture of the iris. A problem is that there is more difference between images of the same face with, e.g., different expression or illumination, than there sometimes is between images of different faces. Face recognition by the brain is discussed     

Face recognition algorithms surpass humans matching faces over changes in illumination

There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based face recognition systems with humans. We compared seven state-of-the-art face recognition algorithms with humans on a face-matching task. Humans and algorithms determined whether pairs of face images, taken under different illumination conditions, were pictures of the same person or of different people. Three algorithms surpassed human performance matching face pairs prescreened to be "difficult" and six algorithms surpassed humans on "easy" face pairs. Although illumination variation continues to challenge face recognition algorithms, current algorithms compete favorably with humans. The superior performance of the best algorithms over humans, in light of the absolute performance levels of the algorithms, underscores the need to compare algorithms with the best current control-humans.     

Face recognition: a convolutional neural-network approach

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.     

Face recognition across pose: A review

One of the major challenges encountered by current face recognition techniques lies in the difficulties of handling varying poses, i.e., recognition of faces in arbitrary in-depth rotations. The face image differences caused by rotations are often larger than the inter-person differences used in distinguishing identities. Face recognition across pose, on the other hand, has great potentials in many applications dealing with uncooperative subjects, in which the full power of face recognition being a passive biometric technique can be implemented and utilised. Extensive efforts have been put into the research toward pose-invariant face recognition in recent years and many prominent approaches have been proposed. However, several issues in face recognition across pose still remain open, such as lack of understanding about subspaces of pose variant images, problem intractability in 3D face modelling, complex face surface reflection mechanism, etc. This paper provides a critical survey of researches on image-based face recognition across pose. The existing techniques are comprehensively reviewed and discussed. They are classified into different categories according to their methodologies in handling pose variations. Their strategies, advantages/disadvantages and performances are elaborated. By generalising different tactics in handling pose variations and evaluating their performances, several promising directions for future research have been suggested.     

An investigation of the modified direction feature for cursive character recognition

This paper describes and analyses the performance of a novel feature extraction technique for the recognition of segmented/cursive characters that may be used in the context of a segmentation-based handwritten word recognition system. The modified direction feature (MDF) extraction technique builds upon the direction feature (DF) technique proposed previously that extracts direction information from the structure of character contours. This principal was extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image.In order to improve on the DF extraction technique, a number of modifications were undertaken. With a view to describe the character contour more effectively, a re-design of the direction number determination technique was performed. Also, an additional global feature was introduced to improve the recognition accuracy for those characters that were most frequently confused with patterns of similar appearance. MDF was tested using a neural network-based classifier and compared to the DF and transition feature (TF) extraction techniques. MDF outperformed both DF and TF techniques using a benchmark dataset and compared favourably with the top results in the literature. A recognition accuracy of above 89% is reported on characters from the CEDAR dataset.     

A random direction algorithm for an intersection problem

A nonlinear SOR-type algorithm is established to find the intersection of a finite number of closed convex sets in a Hilbert space.     

面向光照可变的人脸识别方法

传统Retinex算法在侧光严重的情况下难以消除阴影,为此提出一个对数形式的传导函数,取得了很好的光照补偿效果。为提高人脸识别率,将该问题看成一个典型的模式分类问题,提出基于局部二值模式(LBP)特征的支持向量机(SVM)人脸识别方法,使用“一对一”的方法将多类问题转化为SVM分类器可以解决的两类问题,实现了高效的人脸识别。在CMU PIE、AR、CAS-PEAL以及自行采集的人脸库上进行了仿真实验,结果表明该方法能够有效地去除光照影响,相对传统方法具有较优的识别性能。     

Face recognition using the second-order mixture-of-eigenfaces method

The well-known eigenface method uses an eigenface set obtained from principal component analysis. However, the single eigenface set is not enough to represent the complicated face images with large variations of poses and/or illuminations. To overcome this weakness, we propose a second-order mixture-of-eigenfaces method that combines the second-order eigenface method (ISO MPG m5750, Noordwijkerhout, March 2000) and the mixture-of-eigenfaces method (a.k.a. Gaussian mixture model (Proceedings IJCNN2001, 2001). In this method, we use a couple of mixtures of multiple eigenface sets: one is a mixture of multiple approximate eigenface sets for face images and another is a mixture of multiple residual eigenface sets for residual face images. Each mixture of multiple eigenface sets has been obtained from expectation maximization learning consecutively. Based on two mixture of multiple eigenface sets, each face image is represented by a couple of feature vectors obtained by projecting the face image onto a selected approximate eigenface set and then by projecting the residual face image onto a selected residual eigenface set. Recognition is performed by the distance in the feature space between the input image and the template image stored in the face database. Simulation results show that the proposed second-order mixture-of-eigenfaces method is best for face images with illumination variations and the mixture-of-eigenfaces method is best for the face images with pose variations in terms of average of the normalized modified retrieval rank and false identification rate.     

Face recognition Face2vec based on deep learning: Small database case

Object classification is a common problem in artificial intelligence and now it is usually approached by deep learning. In the paper the artificial neural network (ANN) architecture is considered. According to described ANN architecture, the ANN models are trained and tested on a relatively small Color-FERET facial image database under different conditions. The best fine-tuned ANN model provides 94% face recognition accuracy on Color-FERET frontal images and 98% face recognition accuracy within 3 attempts. However, for improving recognition system accuracy large data sets are still necessary preferably consisting of millions of images.     

人脸识别:从二维到三维

人脸识别是生物特征识别技术的一个重要方向。虽然目前大部分研究都还只是针对二维人脸图像,但是3D人脸模型包含更丰富的人脸信息,有助于机器对人脸的识别。从二维到三维,人脸识别研究进入了一个新的阶段。从3D人脸数据的获取方式入手,介绍最近提出的一系列3D人脸识别算法,并进行归类。最后提出"有针对性地获取3D人脸模型数据是进行有效识别的基础"这一结论。     

Wireless Face Interface: Using voluntary gaze direction and facial muscle activations for human-computer interaction

The present aim was to investigate the functionality of a new wireless prototype called Face Interface. The prototype combines the use of voluntary gaze direction and facial muscle activations, for pointing and selecting objects on a computer screen, respectively. The subjective and objective functionality of the prototype was evaluated with a series of pointing tasks using either frowning (i.e., frowning technique) or raising the eyebrows (i.e., raising technique) as the selection technique. Pointing task times and accuracies were measured using three target diameters (i.e., 25, 30, 40 mm), seven pointing distances (i.e., 60, 120, 180, 240, 260, 450, and 520 mm), and eight pointing angles (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°). The results showed that the raising technique was faster selection technique than the frowning technique for the objects that were presented in the pointing distances from 60 mm to 260 mm. For those pointing distances the overall pointing task times were 2.4 s for the frowning technique, and 1.6 s for the raising technique. Fitts’ law computations showed that the correlations for the Fitts’ law model were r = 0.77 for the frowning technique and r = 0.51 for the raising technique. Further, the index of performance (IP) value was 1.9 bits/s for the frowning technique and 5.4 bits/s for raising the eyebrows technique. Based on the results, the prototype functioned well and was adjustable so that two different facial activations can be used in combination with gaze direction for pointing and selecting objects on a computer screen.     

基于残差网络的风向自记图识别方法

鉴于气象资料风向自记图中存在背景文字干扰和特征所在区域固定等特点,导致卷积神经网络只考虑风向自记图局部近邻特征的问题,使卷积神经网络不能准确识别风向自记图.针对上述存在问题,提出残差网络和自注意力机制相结合的风向自记图识别模型.采用一维和二维风向自记图作为输入数据,通过残差网络进行风向自记图特征提取,引入自注意力机制对...     

消除光照和姿态变化影响的人脸识别

光照和姿态变化带来的影响是自动人脸识别的两个主要瓶颈问题。提出了消除这两方面影响的处理方法：首先对训练集里的图像应用灰度归一化处理,降低对光照强度的敏感度;然后进行姿态估计,并用特征脸方法计算不同姿态的特征子空间,最后提出了“姿态权重PWV（Pose’s Weight Value）”这一概念,据此设计了加权的最小距离分类器WMDC（Weighted Minimum Distance Classifier）,分配不同姿态权重消除姿态变化影响。在FERET和Yale B数据库上的实验结果表明,此方法能在很大程度上提高人脸光照和姿态改变时的识别率。     

Face recognition using the nearest feature line method

We propose a classification method, called the nearest feature line (NFL), for face recognition. Any two feature points of the same class (person) are generalized by the feature line (FL) passing through the two points. The derived FL can capture more variations of face images than the original points and thus expands the capacity of the available database. The classification is based on the nearest distance from the query feature point to each FL. With a combined face database, the NFL error rate is about 43.7-65.4% of that of the standard eigenface method. Moreover, the NFL achieves the lowest error rate reported to date for the ORL face database.