基于深度学习与特征融合的人脸识别算法

卷积神经网络在人脸识别研究上有较好的效果,但是其提取的人脸特征忽略了人脸的局部结构特征。针对此问题,文中提出一种基于深度学习与特征融合的人脸识别方法。该算法将局部二值模式信息与原图信息相结合作为SDFVGG网络的输入,使得提取的人脸特征更加丰富且更具表征能力。其中,SDFVGG网络是将VGG网络进行深浅特征相融合后的网络。在CAS-PEAL-R1人脸数据库上的实验表明,将网络深浅特征相融合与在卷积神经网络中加入LBP图像信息与原图信息相融合的特征信息对于提高人脸识别准确率非常有效,可得到优于传统算法和一般卷积神经网络的最高98.58%人脸识别率。     

融合局部语义与全局信息的人脸表情识别

人脸表情识别在人机交互等人工智能领域发挥着 重要作用,当前研究忽略了人脸的语 义信息。本 文提出了一种融合局部语义与全局信息的人脸表情识别网络,由两个分支组成:局部语义区 域提取分支 和局部-全局特征融合分支。首先利用人脸解析数据集训练语义分割网络得到人脸语义解析 ,通过迁移训 练的方法得到人脸表情数据集的语义解析。在语义解析中获取对表情识别有意义的区域及其 语义特征, 并将局部语义特征与全局特征融合,构造语义局部特征。最后,融合语义局部特征与全局特 征构成人脸 表情的全局语义复合特征,并通过分类器分为7种基础表情之一。本文同时提出了解冻部分 层训练策略, 该训练策略使语义特征更适用于表情识别,减 少语义信息冗余性。在两个公开数据集JAFFE 和KDEF上 的平均识别准确率分别达到了93.81%和88.78% ,表现优于目前的深度学习方法和传统方法。实验结果证 明了本文提出的融合局部语义和全局信息的网络能够很好地描述表情信息。     

An Exhaustive Multi Factor Face Authentication Using Neuro-Fuzzy Approach

The face authentication is a challenging task to validate the user with uncontrolled environment like variations on expression, pose, illumination and occlusion. In order to address these issues, the proposed work provides solution by considering all these factors in inter and intra personal face authentication. During enrollment process, the facial region of still image for the authorized user is detected and features are extracted using local tetra pattern (LTrP) technique. The features are given as input to the neural network namely fuzzy adaptive learning control network (FALCON) for training and classification of features. During authentication process, an image that can vary with expression, pose, illumination and occlusion factors is taken as test image and the test image is applied with LTrP and FALCON to train the features of test image. Then, these trained features are compared with existing feature set by using new proposed multi factor face authentication algorithm to authenticate a person. This work is evaluated among 1150 face images which are collected from JAFFE, Yale, ORL and AR datasets. The overall performance of the work is evaluated by authenticating 1106 images from 1150 constrained images. The second phase of the research work finally produces highest recognition rate of 96% among conventional methods.

     

基于多尺度细节增强的面部表情识别方法

人类面部表情是其心理情绪变化的最直观刻画,不同人的面部表情具有很大差异,现有表情识别方法均利用面部统计特征区分不同表情,其缺乏对于人脸细节信息的深度挖掘。根据心理学家对面部行为编码的定义可以看出,人脸的局部细节信息决定了其表情意义。因此该文提出一种基于多尺度细节增强的面部表情识别方法,针对面部表情受图像细节影响较大的特点,提出利用高斯金字塔提取图像细节信息,并对图像进行细节增强,从而强化人脸表情信息。针对面部表情的局部性特点,提出利用层次结构的局部梯度特征计算方法,描述面部特征点局部形状特征。最后,使用支持向量机(SVM)对面部表情进行分类。该文在CK+表情数据库中的实验结果表明,该方法不仅验证了图像细节对面部表情识别过程的重要作用,而且在小规模训练数据下也能够得到非常好的识别结果,表情平均识别率达到98.19%。     

人脸显性特征的融合构造方法及识别

 目前的人脸识别研究中,面部几何特征没有得到很好的利用.本文阐述了几何特征对于人脸识别的重要性,在此基础上提出了一种提取面部几何特征的新方法;通过融合几何信息和纹理信息构造出一种面部显性特征,并给出了相应的人脸识别方法.这种新的人脸识别方法相对于基于统计学习的子空间方法具有一定的优势,同时也可作为后者的有益补充.实验表明,本文提出的人脸表示特征及识别方法对人脸表情变化和环境光照变化均有一定的鲁棒性.     

基于统计学习优化SIFT的面部遮挡人脸识别

针对传统的人脸识别算法受面部遮挡的影响导致很难兼顾鲁棒性和保持原始图像核心信息的问题,本文提出了一种基于统计学习优化尺度不变特征变换的面部遮挡人脸识别算法。首先,利用SIFT将所有给定训练图像用一组局部特征描述符表示出来;然后,通过执行统计学习获得正常脸部图像SIFT特征的概率分布函数,利用获得的概率分布函数在新观察到的测试图像中检测异常SIFT特征;最后,计算测试图像与训练图像之间的相似度,并利用K近邻分类器完成人脸识别。在AR人脸数据库上的实验验证了本文算法的有效性及可靠性,实验结果表明,相比其它几种较为先进的人脸识别算法,本文算法取得了更强的识别鲁棒性。     

Deep low-rank feature learning and encoding for cross-age face recognition

Cross-age face recognition (CAFR) is a challenging task, due to significant intra-personal variations. Furthermore, the training and testing data may contain random noise components. To address these issues, this paper proposes a deep low-rank feature learning and encoding method. Firstly, our method employs manifold learning in the low-rank optimization, which preserves the global and local structure of the data samples, while learning the clean low-rank features. Secondly, we encode the low-rank features using our locality-constrained feature encoding method, which learns an age-insensitive codebook from training data, and enables the intra-class samples to share the same local bases in a codebook. In the testing stage, the gallery and probe features are encoded by the learned codebook, which represents the images of the same identity by similar codewords for recognition. Furthermore, the periocular region of human faces is investigated for CAFR. Extensive experiments on five datasets demonstrate the effectiveness of our method.     

基于小波分解和支持向量机的准正面人脸识别方法

基于小波分解提取人脸特征技术和多分类支持向量机模型，提出了一种新的准正面人脸识别算法。小波分解提取人脸特征具有对表情变化不敏感的特点；支持向量机作为分类器被认为具有很高的推广(generalization)性能，无需先验知识。在所提出的算法中，首先对训练图像进行预处理，然后使用小波分解方法对人脸图像进行特征提取，用所提取的人脸特征向量训练多分类支持向量机模型，最后用训练好的支持向量机进行人脸识别。利用ORL人脸图像库对该算法的实验测试结果，以及与其它人脸识别方法的比较结果表明了该算法在识别性能方面的优越性。     

A comparative study of local matching approach for face recognition.

In contrast to holistic methods, local matching methods extract facial features from different levels of locality and quantify them precisely. To determine how they can be best used for face recognition, we conducted a comprehensive comparative study at each step of the local matching process. The conclusions from our experiments include: (1) additional evidence that Gabor features are effective local feature representations and are robust to illumination changes; (2) discrimination based only on a small portion of the face area is surprisingly good; (3) the configuration of facial components does contain rich discriminating information and comparing corresponding local regions utilizes shape features more effectively than comparing corresponding facial components; (4) spatial multiresolution analysis leads to better classification performance; (5) combining local regions with Borda count classifier combination method alleviates the curse of dimensionality. We implemented a complete face recognition system by integrating the best option of each step. Without training, illumination compensation and without any parameter tuning, it achieves superior performance on every category of the FERET test: near perfect classification accuracy (99.5%) on pictures taken on the same day regardless of indoor illumination variations, and significantly better than any other reported performance on pictures taken several days to more than a year apart. The most significant experiments were repeated on the AR database, with similar results.     

基于双层异构深度神经网络模型的人脸识别关键技术研究

伴随着人工智能的快速发展,人脸识别技术在社会领域和工业领域都呈现出较广泛的应用潜力空间,但由于传统人脸识别技术识别率低,识别速度慢,对环境要求非常高,迫切需要革新方法.本文旨在研究如何将深度学习算法引入人脸识别领域,通过构建双层异构深度神经网络模型,模拟神经网络进行学习,使用CNN与DBN等众多模型让计算机逐渐根据大量数据特征学会识别图像与人脸,并对人脸识别领域关键技术难点进行深入研究,从而大幅度提升人脸识别技术的识别率与鲁棒性.     

Improving Deep Learning Feature with Facial Texture Feature for Face Recognition

Face recognition in the reality, is a challenging problem, due to varieties in illumination, background, pose etc. Recently, the deep learning based face recognition algorithm is able to learn effective face features to obtain a very impressive performance. However, this kind of face recognition algorithm completely relies on the machine learning based face features, while ignores the useful experience in hand-craft features which have been studied in a long period. Therefore, a face recognition based on facial texture feature aided deep learning feature (FTFA-DLF) is proposed in this paper. The proposed FTFA-DLF is able to combine the benefits of deep learning and hand-craft features. In the proposed FTFA-DLF method, the hand-craft features are texture features extracted from the eyes, nose, and mouth regions. Then, the hand-craft features are used to aid deep learning features by adding both deep learning and hand-craft features into the objective function layer, which adaptively adjusts the deep learning features so that it can better cooperate with the hand-craft features and obtain a better face recognition performance. Experimental results show that the proposed face recognition algorithm on the LFW face database to achieve the accuracy rate of 97.02%.     

Utility Preserved Facial Image De-identification Using Appearance Subspace Decomposition

Automated human facial image de-identification is a much-needed technology for privacy-preserving social media and intelligent surveillance ap-plications. We propose a novel utility preserved facial image de-identification to subtly tinker the appearance of facial images to achieve facial anonymity by creating"averaged identity faces". This approach is able to preserve the utility of the facial images while achieving the goal of privacy protection. We explore a decomposition of an Active appearance model (AAM) face space by using subspace learning where the loss can be modeled as the difference between two trace ratio items, and each respectively models the level of discriminativeness on identity and utility. Finally, the face space is decomposed into subspaces that are respectively sensitive to face identity and face utility. For the subspace most relevant to face identity, a k-anonymity de-identification procedure is applied. To verify the performance of the proposed facial image de-identification approach, we evaluate the created"averaged faces"using the extended Cohn-Kanade Dataset (CK+). The experimental results show that our proposed approach is satisfied to preserve the utility of the original image while defying face identity recognition.     

采用局部二值模式与深度信念网络的人脸识别

针对人脸维度过高和人脸局部特征提取易忽略的问题,提出了一种将多尺度局部二值模式( LBP)算法与深度信念网络( DBN)算法相结合的人脸识别方法。首先采用多尺度LBP算法提取人脸纹理特征,进而将LBP提取的纹理特征作为深度信念网络的输入,最后通过逐层网络训练,得到网络的最优参数,并在ORL人脸库中进行测试,识别率可达95.2%,比使用Gabor小波和主成分分析(PCA)算法的人脸识别高2.6%,说明该算法具有很好的人脸识别能力。     

人脸识别技术研究进展分析

人脸识别技术主要是生物识别技术中的一种,其工作原理是利用人脸的五官、肤色进行身份的识别。通过视频采集设备采集人脸的视频,并且对视频中的人脸进行检测,从实现人脸的识别。人脸识别技术是人工智能研究的主要方向,因此要注重人脸识别技术研究现状,阐述人脸识别技术的相关特点,分析人脸识别方法,确定人脸识别技术的发展方向。     

面部表情跟踪描绘中的边缘特征量

由于外界环境的不确定性和人脸的复杂性,人脸表情的跟踪与计算机形象描绘是一个较难问题.基于此问题,提出了一种有别于模式识别、样本学习等传统手段的较为简单解决方法,在视频采集条件下,分析帧图像,通过对比多种边缘检测方法,采用一种基于边缘特征提取的人脸表情建模方法,来完成用于表情描绘的面部特征量提取与建模,并结合曲线拟合和模型控制等手段,进行人脸卡通造型生成和二维表情动画模拟.实现了从输入数据生成卡通造型画并真实地表现出表情变化情况.     

基于PCA特征提取和距离哈希K近邻分类的人脸表情识别

使用PCA降维,提取人脸表情特征,并结合基于距离的哈希K近邻分类算法进行人脸表情识别。首先使用类Haar特征和AdaBoost算法进行人脸检测,并对人脸图像进行预处理;接着使用PCA提取人脸表情特征,并将特征加入到哈希表;最后使用K近邻分类算法进行人脸表情的识别。将特征库重构为哈希表后,很大地提高了识别效率。     

Multimodal emotion recognition based on peak frame selection from video

We present a fully automatic multimodal emotion recognition system based on three novel peak frame selection approaches using the video channel. Selection of peak frames (i.e., apex frames) is an important preprocessing step for facial expression recognition as they contain the most relevant information for classification. Two of the three proposed peak frame selection methods (i.e., MAXDIST and DEND-CLUSTER) do not employ any training or prior learning. The third method proposed for peak frame selection (i.e., EIFS) is based on measuring the “distance” of the expressive face from the subspace of neutral facial expression, which requires a prior learning step to model the subspace of neutral face shapes. The audio and video modalities are fused at the decision level. The subject-independent audio-visual emotion recognition system has shown promising results on two databases in two different languages (eNTERFACE and BAUM-1a).     

应用于人脸识别的监督局部邻域保持嵌入算法

提出了一种应用于人脸识别的监督线性维数约简 算法。首先引入图像距离度量方法以确定人脸数据 之间的相似程度,之后将训练样本的类标先验信息融入到邻域保持嵌入(NPE,neighborhood preserving embedding)算法的目标函 数中,使得降维后的嵌入空间的投影数据呈多流形分布,不仅最优保持了样本空间的局部几 何结构,同时各类样本 投影的类内散度最小化,类间散度最大化,增大了各类数据分布之间的间隔,提高了嵌入空 间的辨别能力。在Extended Yale B和CMU PIE两个开放人脸数据库上进行了识别实验,结果表明,本文算法取得了很好 的识别效果。     

Facial micro-expression recognition based on accordion spatio-temporal representation and random forests

Micro-expressions are very brief involuntary facial expressions which appear on the face of humans when they unconsciously conceal an emotion. Creating a solution allowing an automatic recognition of the facial micro-expressions from video sequences has garnered increasing attention from experts across such different disciplines as computer science, security, and psychology. This paper offered a solution to facial micro-expressions recognition, based on accordion spatio-temporal representation and Random Forests. The proposed feature space, called “Uniform Local Binary Patterns on an Accordion 2D representation of sub-regions presented by a Pyramid of levels (LBPAccP^u2)”, exploits the effectiveness of uniform LBP patterns applied on an accordion representation of sub-regions at different sizes. Random Forests were used to select the most discriminating features and reduce the classification ambiguity of similar micro-expressions through a new proximity measure. The main objective of our paper was to demonstrate that the use of few features could be more efficient to produce a strong micro-expression recognition classifier that outperforms the approaches that rely on high dimensional features space. The experimental results across six micro-expression datasets show the effectiveness of the proposed solution with an accuracy rate that can reach 81.38% on CasmeII dataset. Compared to some famous competitive state-of-the-art approaches, the proposed solution proved its performance thanks to its accuracy rate as well as the number of features it uses.