一种新颖的测地距傅立叶轮廓描述符

在传统的用于图像检索的傅里叶轮廓描述符的基础上,提出了一种新颖的、基于图像轮廓的测地距傅里叶描述符,并在标准图像轮廓数据库MPEG-7上进行了测试。实验结果表明这种轮廓描述符在性能上优于其他基于图像轮廓的傅里叶描述符。     

Learning deep compact similarity metric for kinship verification from face images

Recent advances in kinship verification have shown that learning an appropriate kinship similarity metric on human faces plays a critical role in this problem. However, most of existing distance metric learning (DML) based solutions rely on linearity assumption of the kinship metric model, and the domain knowledge of large cross-generation discrepancy (e.g., large age span and gender difference between parent and child images) has not been considered in metric learning, leading to degraded performance for genetic similarity measure on human faces. To address these limitations, we propose in this work a new kinship metric learning (KML) method with a coupled deep neural network (DNN) model. KML explicitly models the cross-generation discrepancy inherent on parent-child pairs, and learns a coupled deep similarity metric such that the image pairs with kinship relation are pulled close, while those without kinship relation (but with high appearance similarity) are pushed as far away as possible. Moreover, by imposing the intra-connection diversity and inter-connection consistency over the coupled DNN, we introduce the property of hierarchical compactness into the coupled network to facilitate deep metric learning with limited amount of kinship training data. Empirically, we evaluate our algorithm on several kinship benchmarks against the state-of-the-art DML alternatives, and the results demonstrate the superiority of our method.     

Ensemble similarity learning for kinship verification from facial images in the wild

Kin relationship has been well investigated in psychology community over the past decades, while kin verification using facial images is relatively new and challenging problem in biometrics society. Recently, it has attracted substantial attention from biometrics society, mainly motivated by the relative characteristics that children generally resemble their parents more than other persons with respect to facial appearance. Unlike most previous supervised metric learning methods focusing on learning the Mahalanobis distance metric for kin verification, we propose in this paper a new Ensemble similarity learning (ESL) method for this challenging problem. We first introduce a sparse bilinear similarity function to model the relative characteristics encoded in kin data. The similarity function parameterized by a diagonal matrix enjoys the superiority in computational efficiency, making it more practical for real-world high-dimensional kinship verification applications. Then, ESL learns from kin dataset by generating an ensemble of similarity models with the aim of achieving strong generalization ability. Specifically, ESL works by best satisfying the constraints (typically triplet-based) derived from the class labels on each base similarity model, while maximizing the diversity among the base similarity models. Experiments results demonstrate that our method is superior to some state-of-the-art methods in terms of both verification rate and computational efficiency.     

基于旋转不变局部相位量化特征的人脸确认算法研究*

针对非受控环境下人脸图像的采集易受光照、姿态、表情、遮挡的影响且成像质量低等为人脸确认带来很大困难这一问题,提出了采用旋转不变局部相位量化(RILPQ)特征算子结合学习度量距离的方法进行人脸确认。首先利用RILPQ特征算子对待确认的两幅图像分别提取RILPQ编码图像;然后分块获得空间区域RILPQ直方图序列并进行PCA降维,并将降维后的RILPQ直方图序列作为人脸图像的特征描述子,计算两幅人脸图像描述特征的统计Fisher加权距离;最后采用SVM进行人脸确认。在LFW人脸库上的实验表明该方法在同类算法中具有最好的性能。     

Siamese convolutional neural network and fusion of the best overlapping blocks for kinship verification

Multimedia Tools and Applications - Analysis of facial images decoding familial features has been attracting the attention of researchers to develop a computerized system interested in determining...     

Automated kinship verification and identification through human facial images: a survey

Face is the most considerable constituent that people use to recognize one another. Humans can quickly and easily identify each other by their faces and since facial features are unobtrusive to lighting condition and pose, face remains as a dynamic recognition approach to human. Kinship recognition refers to the task of training a machine to recognize the blood relation between a pair of kin and non-kin faces (verification) based on features extracted from facial images, and to determine the exact type or degree of that relation (identification). Automatic kinship verification and identification is an interesting areas for investigation, and it has a significant impact in many real world applications, for instance, forensic, finding missing family members, and historical and genealogical research. However, kinship recognition is still not largely explored due to insufficient database availability. In this paper we present a survey on issues and challenges in kinship verification and identification, related previous works, current trends and advancements in kinship recognition, and potential applications and research direction for the future. We also found that Deep Learning (DL) has mostly outperformed numerous methods using manually designed features by automatically learning and extracting important information from facial features, and enable significant visual recognition functions by improving accuracy in most applications.     

Online signature verification based on string edit distance

International Journal on Document Analysis and Recognition (IJDAR) - Handwritten signatures are widely used and well-accepted biometrics for personal authentication. The accuracy of signature...     

列车安全距离控制形式化建模与验证

随着我国铁路的迅速发展,对列车运行安全性的要求越来越高。采用Event-B形式化建模方法研究了高速列车安全距离控制形式化验证问题,以Event-B形式化仿真工具Rodin为基础,通过结合多智能体理论,引入感知决策法则,实现了无线闭塞中心(RBC)与列车的车地通信,建立了多列车运行的安全距离控制模型。仿真研究了高速列车最小间隔追踪控制运行,对列车安全距离控车行为进行了形式化建模并进行了POs证明义务验证。仿真结果表明,对于CTCS列车控制系统的复杂逻辑关联行为,采用提出的Event-B和多智能体系统(MAS)结合的形式化验证方法,可进行系统规范的模型验证,对于复杂系统的逻辑验证有较强的实际意义。     

Variational calculus for descriptor problems

The first-order, necessary condition for optimality is derived from a variational argument that involves an ad hoc modification of the Bliss method, resulting in a Hamiltonian characterization in terms of Edx,dt, rather than dx/dt, the former being smoother than the latter. This approach sidesteps the regularity conditions of the Lagrange multiplier theory. Under some mild assumptions, the necessary condition for optimality is also sufficient and the optimal control exists. The numerically relevant result is a generalized eigenvector, inverse-free characterization of optimality