First congenital prosopagnosia case reported in China

Congenital prosopagnosia (CP) refers to the deficiency in recognizing faces without reportable brain damage. In this study we reported a CP patient (X.L.), the first CP case reported in China, and tested her with a battery of face recognition and visual cognition tasks to explore what aspects of face processing are impaired in CP individuals. Our results show that X.L. possesses a normal ability to detect the presence of faces yet her ability to discriminate between individual faces is significantly impaired compared to normal controls. In contrast, her ability to detect nonface objects is greatly preserved, suggesting that she suffers from a selective deficiency in face recognition. Further face tests reveal that she has intact feature-based analysis ability while configural and holistic processing are both impaired. We further evaluate her general capacity of holistic processing, and the results showed that her impairment in holistic processing is not restricted to faces, but to a more general level.     

A mechanism for impaired fear recognition after amygdala damage

Ten years ago, we reported that SM, a patient with rare bilateral amygdala damage, showed an intriguing impairment in her ability to recognize fear from facial expressions. Since then, the importance of the amygdala in processing information about facial emotions has been borne out by a number of lesion and functional imaging studies. Yet the mechanism by which amygdala damage compromises fear recognition has not been identified. Returning to patient SM, we now show that her impairment stems from an inability to make normal use of information from the eye region of faces when judging emotions, a defect we trace to a lack of spontaneous fixations on the eyes during free viewing of faces. Although SM fails to look normally at the eye region in all facial expressions, her selective impairment in recognizing fear is explained by the fact that the eyes are the most important feature for identifying this emotion. Notably, SM's recognition of fearful faces became entirely normal when she was instructed explicitly to look at the eyes. This finding provides a mechanism to explain the amygdala's role in fear recognition, and points to new approaches for the possible rehabilitation of patients with defective emotion perception.     

基于DCT和BP神经网络集成的人脸识别

人脸识别是模式识别和图像处理领域的研究热点和难点,尽管已提出了许多方法,然而如何在变化的环境下实时、高效地识别人脸仍是一个难题.鉴于离散余弦变换(discrete cosine transform,DCT)有较高的压缩性能和快速算法以及集成神经网络良好的泛化能力,提出了基于DCT和神经网络集成的人脸识别方法.首先用DCT提取人脸特征矢量,然后构建集成BP(back propagation)神经网络对人脸进行分类识别.在ORL(olivertti research laboratory)人脸库上的仿真实验结果表明,提出的方法取得了较快的训练和识别速度、较高的识别率,因此该方法是一种快速高效的人脸识别方法.     

Selective impairment in recognizing the familiarity of self faces in schizophrenia

The concept of self is a fundamental characteristic of the human mind,and the alteration of self is thought to be a core deficit of schizophrenia.Previous studies have demonstrated that patients with schizophrenia are deficient in self-face recognition.Because self faces are not only self-related but also highly familiar,it is unclear whether such deficit arises from the breakdown of the self-awareness or the failure of recognizing the familiarity of self faces.Here we directly tested these two alternatives by instructing patients with schizophrenia to recognize the identity of a morphed face created by blending face features between any of two identities from the self face,a familiar face,and a novel face.We found that there was no association between the recognition of the self and the recognition of the familiarity,suggesting these two component processes are independent in schizophrenia.Further,patients with schizophrenia were significantly worse in recognizing the familiarity of faces than normal participants,whereas no difference in the sense of self was found between the two groups.Taken together,our finding suggests that it is the sense of familiarity,not the sense of self,that is selectively impaired in self-face recognition in schizophrenia.Thus,our study challenges the hypothesis that the deficit in self-face recognition in schizophrenia reflects the breakdown of self-awareness.     

人脸识别系统的设计与实现

随着社会信息化、网络化的发展,自动身份认证逐渐成为一种趋势,人脸识别以其特有的便利性成为当前研究的热点.在学习前人的图像处理技术基础上,综合考虑各方面条件,在算法上采用人脸识别领域中的HAAR方法对人脸进行检测,采用PCA方法进行人脸特征提取;在图像显示上采用JavaWeb开发的SSH框架对图像进行处理,将C++图像处理的便利条件和Java Web开发的友好效果结合起来.最终实现的功能是:从人脸图像库中找出与上传人脸最相似的前三个人脸图片,并显示他们的信息.     

基于特征脸的人脸识别系统

提出了一种快速有效的人脸识别系统．针对特征脸方法只能识别标准正面人脸的局限性，设计了前端处理模块，首先计算待识别图像中人脸的倾斜角度，进行相应角度的旋转，然后剪切出人脸图像，进行尺寸调整，经过此处理后再进行特征脸识别．利用ORL人脸库和自建人脸库进行仿真实验，实验结果表明，本识别系统速度快，误识率低，具有实用性．     

基于改进R-LDA的ANN在人脸识别中的研究

面部识别(FR)系统可以自动识别或校验从数码相机或图像生成设备中获得的人脸图像。为了做到这点,要从所获图像中提取面部特征,并与人脸数据库中的数据进行比对。目前,几乎所有的FR都面临与面部视角相关的障碍,包括光照不足和低分辨率,这些问题使其识别率大为降低。提出了一种经过全新衡量的标准化参数,它基于FR系统,能够提高在某些环境约束下的识别率。该方法基于常见的正规线性判别分析(R-LDA),并且包含了具有突出分类能力的可以提高人脸识别率的人工神经网络(ANN)。改进的R-LDA算法解决了在所有FR中出现的小样本容量(SSS)问题,同时,ANN对于检测人脸的正面图像很有用处。在ORL及FERET人脸数据库上进行了实验,结果表明,与其它的常用方法相比较,取得了更好的识别效果。     

神经网络和遗传算法在人脸定位中的应用

人脸是十分常见的复杂模式,在复杂景物图片中自动找出人脸是很困难的,但是却有着重要意义,因为这同时也是自动人脸识别系统的前提．提出了一个基于多层感知器（ＭＬＰ）的用遗传算法实现搜索的人脸定位系统,讨论了系统建立的理论基础,即ＭＬＰ直接感知图象和用遗传算法进行快速搜索．实验结果表明,它尤其适合在复杂背景下的低分辨图象中进行操作,鲁棒性好、定位快、适应面宽．     

基于广义典型相关分析融合和鲁棒概率协同表示的人脸指纹多模态识别

针对单模态生物特征识别容易受自身条件和环境变化的影响,鉴于人脸识别和指纹识别已经在生物识别系统中得到了广泛应用,提出了二者特征信息融合的多模态生物特征识别方法。该方法首先对人脸、指纹图像进行预处理,并对这两种模态均提取LBP和Gabor特征,然后将广义典型相关分析方法分别引入到人脸多特征融合和指纹多特征融合中,应用分块对角矩阵组合上述融合的人脸特征和指纹特征,最后用鲁棒概率协同表示分类器进行分类。在两个多模态数据库上的实验结果表明:与人脸或指纹单模态生物特征识别相比,基于人脸指纹的多模态生物特征识别具有更高的识别率和更好的稳定性;所提出的基于广义典型相关分析的特征融合方法优于传统的融合方法。     

人脸自动追踪与识别系统的设计与实现

为快速和准确地识别有关人员的身份,开发了一个具有5个自由度的人脸自动追踪与识别系统．该系统采用人脸肤色模型结合相似度及人脸轮廓来检测和定位人脸,并利用嵌入式隐Markov模型对人脸的特征进行提取和识别．实验结果表明,该系统不仅能自动追踪、定位和识别人脸,而且能实时地将识别结果与人脸数据库进行比对,从而迅速地获得被识别人员的有关身份信息．该系统可用于建筑物入口的监控和刑事侦察等场合．     

基于DCT的PCA及神经网络的人脸识别研究

PCA(Principal Component Analysis,也称为K-L变换)方法在许多人脸库的识别中取得了很好的效果,但运算量大;DCT(Discrete Cosine Transform)有较高的压缩性能并可通过快速算法来完成;集成的BP神经网络比单一BP网具有更好的泛化能力.针对上述特点,提出了一种新算法,首先用DCT处理原始人脸图像,然后用包含了原始图像大部分信息的少量DCT系数作PCA,最后用集成的BP神经网络进行识别.在ORL人脸库上的仿真试验结果表明,该方法是一种更为高效的识别方法.     

超越死亡的凯歌——克里斯蒂娜·罗塞蒂诗歌中的死亡主题

对死亡的高度关注贯穿了克里斯蒂娜的诗歌，通过对她诗歌的分析，说明了诗人对死亡的深刻感悟，以及诗人能够克服恐惧、坦然面对死亡并且超越死亡。     

基于XML与人脸识别的智能小区门禁管理系统设计

为实现小区的智能化人性化管理,设计了一套基于XML与人脸识别的智能小区门禁管理系统。首先,集成网络摄像头和数据分析工作站,组成系统硬件平台;嵌入人脸识别算法和XML结构化数据,构建了智能小区门禁管理系统。然后,结合图像灰度处理、直方图均衡化和haar特征检测,设计了人脸图像识别算子,实现人脸有、无来访者身份确定。在Visiual Studio平台开发系统,对所提智能小区门禁管理系统进行了测试,输出结果表明:所提出的智能小区门禁管理系统,在人脸识别和系统智能性方面,都优于传统小区门禁管理系统。     

A study on neural mechanism of face processing based on fMRI

Recently, there were debates about the specificity of lateral middle fuisform in face processing. The debates focused on whether these areas were specialized in face processing or involved in processing of visual expertise and categorization at individual level. The present study aims to investigate the neural mechanism of face processing, using Chinese characters as comparison stimuli. Chinese characters are greatly similar to faces on a variety of dimensions, among which the most significant one is that both faces and Chinese characters not only are extremely familiar to literate Chinese adults but also are processed at individual level. In the present study, faces and Chinese characters activated bilateral middle fusiform with great correlation. Greater activities were observed in the right fusiform face area (FFA) for faces than for Chinese characters. These results demonstrate that FFA is specialized in face processing per se rather than the processing of visual expertise and categorization at individual level.     

新闻图像中重要人物的自动检测和识别研究

针对网络新闻图像中人物的不同表情、光照、姿势等因素带来的视觉空间差异造成的人脸检测效果差的问题,提出滑动窗口自适应方法和肤色特征相融合,检测新闻图像中的人脸。针对单样本条件下的人脸识别问题,提出有监督的双向二维局部保持投影法,提取人脸特征。结合图像子特征加权方法,先将人脸图像分块,子图像的子集作为一个子特征统计权重,采用人为投票的方式进行识别。实验选取雅虎新闻图像和新华网新闻图像。结果表明,滑动窗口自适应算法在不同类别新闻图像中人脸检测率提高了3%到10%,人脸识别率在一定范围内提高了5%。     

A study on neural mechanism of face processing based on fMRI

Recently, there were debates about the specificity of lateral middle fusiform in face processing. The debates focused on whether these areas were specialized in face processing or involved in processing of visual expertise and categorization at individual level. The present study aims to investigate the neural mechanism of face processing, using Chinese characters as comparison stimuli. Chinese characters are greatly similar to faces on a variety of dimensions, among which the most significant one is that both faces and Chinese characters not only are extremely familiar to literate Chinese adults but also are processed at individual level. In the present study, faces and Chinese characters activated bilateral middle fusiform with great correlation. Greater activities were observed in the right fusiform face area （FFA） for faces than for Chinese characters. These results demonstrate that FFA is specialized in face processing per se rather than the processing of visual expertise and categorization at individual level.     

A robust face recognition method using multiple features fusion and linear regression

This paper presents a robust face recognition algorithm by using transform domain-based multiple feature fusion and lin- ear regression. Transform domain-based feature fusion can provide comprehensive face information for recognition, and decrease the effect of variations in illumination and pose. The holistic feature and local feature are extracted by discrete cosine transform and Gabor wavelet transform, respectively. Then the extracted holistic features and the local features are fused by weighted sum. The fused feature values are finally sent to linear regression classifier for recognition. The algorithm is evaluated on AR, ORL and Yale B face databases. Experiment results show that our proposed algo- rithm could be more robust than those single feature-based algo- rithms under pose and expression variations.     

基于人脸检测与跟踪的智能监控系统

针对现有视频监控系统的缺陷,提出一种新的基于人脸检测与跟踪的智能监控系统.利用对称差分算法,自动检测场景中的运动区域,限制搜索范围;然后利用BP神经网络对肤色进行识别,获得候选人脸区域,该方法比固定阈值肤色检测方法具有更强的环境适应能力;经过人脸验证,最终定位图像中的人脸;对检测出的人脸,提出了新的基于肤色信息和维护运动人脸缓冲池的方法,主动跟踪目标人脸.依据检测出的人脸信息和当前的日期、时间,建立相应的监控信息标注数据库,以供后期查询.实验表明,该系统能够实时可靠地检测、跟踪运动人脸,满足特定的监控要求.     

计算机人脸识别技术综述

概述了计算机人脸识别技术的历史及发展现状,讨论了在计算机人脸识别领域占有主流地位的Eigen脸方法(主元素分析方法)、最佳鉴别矢量集法(基于Fisher线性判别准则方法和基于Foley-Sammon变换方法)、Bayesian脸方法、基于傅里叶不变特征法和弹性图匹配法。指出了各个研究方向人脸识别方法, 给出了计算机人脸识别性能评价指标,包括识别率、计算时间、数据存储量和可扩展性等。根据这些性能评价指标,对当前的各种计算机人脸识别技术进行分析评价。讨论结果表明,基于Fisher线性判别准则的最佳鉴别矢量集法,Bayesian脸方法和基于傅里叶不变特征法都有较好的性能,具有一定的应用前景.     

Neural encoding of individual words and faces by the human hippocampus and amygdala

Patients with lesions in the medial temporal lobe (MTL) of the brain, which includes the hippocampus, amygdala and parahippocampal gyrus, are severely impaired in their ability to remember and recognize words or faces which they saw only a short time ago. These lesions also prevent the effect of word repetition on cortical event-related potentials that are associated with these tasks. We have been able to study the response of individual neurons in the human medial temporal lobe to such delayed recognition tasks in epileptic patients undergoing neurosurgery. We found that some MTL neurons preferentially fired on sight of one particular word from a set of ten words used in a memory task, and others fired in response to one particular face. This stimulus-specific firing was maximal during the time that the neocortical event potentials are most sensitive to stimulus repetition, suggesting that the MTL contributes specific information to the cortex during the retrieval of recent memories.