用支持向量机进行中文地名识别的研究

用支持向量机（SVM）方法对中文地名的自动识别进行了探讨，对于舍特征词的地名和非地名用支持向量机进行分类：结合中文地名的特点，抽取地名构词可信度及其前后词的词性作为特征向量的属性，建立了一定规模的训练集，并通过对不同kernel函数的测试，得到了地名分类的机器学习模型．实验表明，对于切分正确的地名，本方法具有良好的效果．     

基于面部表情识别的学习疲劳识别和干预方法

针对网络学习者经常出现的身体或心理上的疲劳或疲惫情绪状态即"学习疲劳"状态,提出了一种基于面部表情识别的学习疲劳识别和干预方法.考虑到网络学习的特点,定义了专注、疲劳和中性3种与学习相关的表情,利用一种基于肤色分割和模版匹配相结合的人脸检测算法检测出网络学习者的人脸区域,然后根据建立的人脸表情面部模型对学习者的面部特征进行提取,主要包括眼睛特征和嘴巴特征,最后采用基于规则的表情分类方法,识别出学习者是否处于学习疲劳状态,并采取相应的情感干预措施.实验结果表明,该方法能够快速识别网络学习者是否处于学习疲劳状态,实现实时学习疲劳干预.     

Recognition in the near infrared spectrum for face,gender and facial expressions

Visible face recognition systems are subjected to failure when recognizing the faces in unconstrained scenarios. So, recognizing faces under variable and low illumination conditions are more important since most of the security breaches happen during night time. Near Infrared (NIR) spectrum enables to acquire high quality images, even without any external source of light and hence it is a good method for solving the problem of illumination. Further, the soft biometric trait, gender classification and non verbal communication, facial expression recognition has also been addressed in the NIR spectrum. In this paper, a method has been proposed to recognize the face along with gender classification and facial expression recognition in NIR spectrum. The proposed method is based on transfer learning and it consists of three core components, i) training with small scale NIR images ii) matching NIR-NIR images (homogeneous) and iii) classification. Training on NIR images produce features using transfer learning which has been pre-trained on large scale VIS face images. Next, matching is performed between NIR-NIR spectrum of both training and testing faces. Then it is classified using three, separate SVM classifiers, one for face recognition, the second one for gender classification and the third one for facial expression recognition. It has been observed that the method gives state-of-the-art accuracy on the publicly available, challenging, benchmark datasets CASIA NIR-VIS 2.0, Oulu-CASIA NIR-VIS, PolyU, CBSR, IIT Kh and HITSZ for face recognition. Further, for gender classification the Oulu-CASIA NIR-VIS, PolyU,and IIT Kh has been analyzed and for facial expression the Oulu-CASIA NIR-VIS dataset has been analyzed.

     

Learning person-specific models for facial expression and action unit recognition

A key assumption of traditional machine learning approach is that the test data are draw from the same distribution as the training data. However, this assumption does not hold in many real-world scenarios. For example, in facial expression recognition, the appearance of an expression may vary significantly for different people. As a result, previous work has shown that learning from adequate person-specific data can improve the expression recognition performance over the one from generic data. However, person-specific data is typically very sparse in real-world applications due to the difficulties of data collection and labeling, and learning from sparse data may suffer from serious over-fitting. In this paper, we propose to learn a person-specific model through transfer learning. By transferring the informative knowledge from other people, it allows us to learn an accurate model for a new subject with only a small amount of person-specific data. We conduct extensive experiments to compare different person-specific models for facial expression and action unit (AU) recognition, and show that transfer learning significantly improves the recognition performance with a small amount of training data.     

Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models

This paper presents a multimodal system for reliable human identity recognition under variant conditions. Our system fuses the recognition of face and speech with a general probabilistic framework. For face recognition, we propose a new spectral learning algorithm, which considers not only the discriminative relations among the training data but also the generative models for each class. Due to the tedious cost of face labeling in practice, our spectral face learning utilizes a semi-supervised strategy. That is, only a small number of labeled faces are used in our training step, and the labels are optimally propagated to other unlabeled training faces. Besides requiring much less labeled data, our algorithm also enables a natural way to explicitly train an outlier model that approximately represents unauthorized faces. To boost the robustness of our system for human recognition under various environments, our face recognition is further complemented by a speaker identification agent. Specifically, this agent models the statistical variations of fixed-phrase speech using speaker-dependent word hidden Markov models. Experiments on benchmark databases validate the effectiveness of our face recognition and speaker identification agents, and demonstrate that the recognition accuracy can be apparently improved by integrating these two independent biometric sources together.     

A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.     

人脸样貌独立判别的协作表情识别算法

为了降低样貌、姿态、眼镜以及表情定义不统一等因素对人脸表情识别的影响,提出一种人脸样貌独立判别的协作表情识别算法。首先,采用自动的人脸检测算法定位、对齐视频每帧的人脸区域,并从人脸视频序列中选择峰值表情的人脸;然后,采用峰值人脸与某个表情类内的所有人脸产生表情类内差异人脸信息,并通过计算峰值表情人脸与表情类内差异人脸的差异信息获得协作的表情表示;最终,采用基于稀疏的分类器与表情表示决定每个人脸表情的标签。采用欧美与亚洲人脸的数据库进行仿真实验,结果表明本算法获得了较好的表情识别准确率,对不同样貌、佩戴眼镜的人脸样本也具有较好的识别效果。     

一种针对无线电信号分类的对抗增强方法

深度学习模型依赖大量带类标的数据作为训练数据,实际应用的各种无线电环境中收集并标记无线电信号需要消耗大量的人力物力,极大地限制了深度学习模型在无线电信号识别中的应用。目前针对数据量不足带来的问题,研究者们主要采用数据增强的方法,即根据一些先验知识,在保持已知信息的前提下,对原始数据进行适当变换达到扩充数据集的效果。具体到分类任务,在保持数据类别不变的前提下,可以对训练集中的每个样本进行变换,如在一定程度内的随机旋转、缩放、裁剪、左右翻转等,这些变换对应着同一个目标在不同角度的观察结果,并且增强效果有限。此外,深度学习作为一个非常复杂的方法,会面对各种安全问题。深度神经网络很容易受到对抗样本的攻击,攻击者可以通过向良性数据中添加特定的扰动,生成对抗样本,使DNN模型出错。虽然这些伪造的样本对人类的判断没有影响,但是对于深度学习模型来说是一个致命性的误导。聚焦到深度学习领域,本论文提出一种针对无线电信号分类的对抗增强方法,将对抗训练方法引入信号领域,通过控制eps、iteration参数,在数据集中添加算法精心设计的细微扰动生成靠近决策边界面的边界样本实现数据增强,将边界样本与训练样本混合,重新训练识别模型,在提升模型识别精度的同时,提升模型的防御能力。最终在多个分类模型、多个实际无线电信号数据集上的分类性能都有显著的提高,同时防御性能也显著增强,验证了本文提出的信号增强识别方法的有效性。关键词深度学习;对抗训练;调制识别;数据增强     

Face recognition by applying wavelet subband representation and kernel associative memory

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.     

采用深度学习方法的非正面表情识别综述

2017年人工智能正式升级为中国国家战略,作为人工智能领域中重要的研究方向,人脸表情识别受到了国内外研究者们的广泛关注。然而传统的人脸表情识别技术无法适应自然环境下的表情识别需求。因此非正面人脸表情识别方法成为实现表情识别技术实用化突破的重点。但是现有的非正面表情识别研究面临很多困难：头部偏转不仅造成了识别图像的扭曲,而且还遮挡了部分人脸区域,严重干扰了表情特征的提取与识别。有鉴于此,研究者们将深度学习技术与非正面表情识别相结合,依靠非正面表情图像的深度信息,实现算法识别能力的提升。综述详细介绍了深度神经网络的结构,对最新的深度学习神经网络研究方法进行分类对比,同时对未来的研究和挑战做了展望。     

基于轻量型卷积神经网络的交通标志识别方法

交通标志识别设备的功耗和硬件性能较低,而现有卷积神经网络模型内存占用高、训练速度慢、计算开销大,无法应用于识别设备.针对此问题,为降低模型存储,提升训练速度,引入深度可分离卷积和混洗分组卷积并与极限学习机相结合,提出两种轻量型卷积神经网络模型:DSC-ELM模型和SGC-ELM模型.模型使用轻量化卷积神经网络提取特征后,将特征送入极限学习机进行分类,解决了卷积神经网络全连接层参数训练慢的问题.新模型结合了轻量型卷积神经网络模型内存占用低、提取特征质量好以及ELM的泛化性好、训练速度快的优点.实验结果表明.与其他模型相比,该混合模型能够更加快速准确地完成交通标志识别任务.     

An Expression Deformation Approach to Non-rigid 3D Face Recognition

The accuracy of non-rigid 3D face recognition approaches is highly influenced by their capacity to differentiate between the deformations caused by facial expressions from the distinctive geometric attributes that uniquely characterize a 3D face, interpersonal disparities. We present an automatic 3D face recognition approach which can accurately differentiate between expression deformations and interpersonal disparities and hence recognize faces under any facial expression. The patterns of expression deformations are first learnt from training data in PCA eigenvectors. These patterns are then used to morph out the expression deformations. Similarity measures are extracted by matching the morphed 3D faces. PCA is performed in such a way it models only the facial expressions leaving out the interpersonal disparities. The approach was applied on the FRGC v2.0 dataset and superior recognition performance was achieved. The verification rates at 0.001 FAR were 98.35% and 97.73% for scans under neutral and non-neutral expressions, respectively.     

An efficient deep learning technique for facial emotion recognition

Emotion recognition from facial images is considered as a challenging task due to the varying nature of facial expressions. The prior studies on emotion classification from facial images using deep learning models have focused on emotion recognition from facial images but face the issue of performance degradation due to poor selection of layers in the convolutional neural network model.To address this issue, we propose an efficient deep learning technique using a convolutional neural network model for classifying emotions from facial images and detecting age and gender from the facial expressions efficiently. Experimental results show that the proposed model outperformed baseline works by achieving an accuracy of 95.65% for emotion recognition, 98.5% for age recognition, and 99.14% for gender recognition.

     

基于激光测距AdaBoost分类的室内环境识别

移动机器人所处环境的地点语义信息能够提高机器人自主定位、路径规划和人机互动的能力.为了让机器人识别环境中不同地点类型,提出一种对机器人所处环境地点类型进行语义分类的方法.该方法对激光传感器的测距数据进行特征提取,通过提取的样本集利用强化学习AdaBoost方法构建分类器,对于环境中多类型地点分类识别,将获得的二分类器有顺序地排列建立分类列表形成多分类器,将获得的多分类器运用到房间、走廊和门口的分类识别中.实验结果表明:移动机器人通过该方法都能对环境下不同地点类型进行有效的分类识别.     

基于对比学习的细粒度遮挡人脸表情识别

和实验室环境不同,现实生活中的人脸表情图像场景复杂,其中最常见的局部遮挡问题会造成面部外观的显著改变,使得模型提取到的全局特征包含与情感无关的冗余信息从而降低了判别力.针对此问题,本文提出了一种结合对比学习和通道-空间注意力机制的人脸表情识别方法,学习各局部显著情感特征并关注局部特征与全局特征之间的关系.首先引入对比学习,通过特定的数据增强方法设计新的正负样本选取策略,对大量易获得的无标签情感数据进行预训练,学习具有感知遮挡能力的表征,再将此表征迁移到下游人脸表情识别任务以提高识别性能.在下游任务中,将每张人脸图像的表情分析问题转化为多个局部区域的情感检测问题,使用通道-空间注意力机制学习人脸不同局部区域的细粒度注意力图,并对加权特征进行融合,削弱遮挡内容带来的噪声影响,最后提出约束损失联合训练,优化最终用于分类的融合特征.实验结果表明,无论是在公开的非遮挡人脸表情数据集(RAFDB和FER2013)还是人工合成的遮挡人脸表情数据集上,所提方法都取得了与现有先进方法可媲美的结果.     

持续学习改进的人脸表情识别

目的 大量标注数据和深度学习方法极大地提升了图像识别性能。然而,表情识别的标注数据缺乏,训练出的深度模型极易过拟合,研究表明使用人脸识别的预训练网络可以缓解这一问题。但是预训练的人脸网络可能会保留大量身份信息,不利于表情识别。本文探究如何有效利用人脸识别的预训练网络来提升表情识别的性能。方法 本文引入持续学习的思想,利用人脸识别和表情识别之间的联系来指导表情识别。方法指出网络中对人脸识别整体损失函数的下降贡献最大的参数与捕获人脸公共特征相关,对表情识别来说为重要参数,能够帮助感知面部特征。该方法由两个阶段组成：首先训练一个人脸识别网络,同时计算并记录网络中每个参数的重要性;然后利用预训练的模型进行表情识别的训练,同时通过限制重要参数的变化来保留模型对于面部特征的强大感知能力,另外非重要参数能够以较大的幅度变化,从而学习更多表情特有的信息。这种方法称之为参数重要性正则。结果 该方法在RAF-DB（real-world affective faces database）,CK+（the extended Cohn-Kanade database）和Oulu-CASIA这3个数据集上进行了实验评估。在主流数据集RAF-DB上,该方法达到了88.04%的精度,相比于直接用预训练网络微调的方法提升了1.83%。其他数据集的实验结果也表明了该方法的有效性。结论 提出的参数重要性正则,通过利用人脸识别和表情识别之间的联系,充分发挥人脸识别预训练模型的作用,使得表情识别模型更加鲁棒。     

Towards generalization of semi-supervised place classification over generalized Voronoi graph

With the progress of human–robot interaction (HRI), the ability of a robot to perform high-level tasks in complex environments is fast becoming an essential requirement. To this end, it is desirable for a robot to understand the environment at both geometric and semantic levels. Therefore in recent years, research towards place classification has been gaining in popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms have been extensively used for this purpose, showing satisfactory performance levels. However, most of those approaches have only been trained and tested in the same environments and thus impede a generalized solution. In this paper, we have proposed a semi-supervised place classification over a generalized Voronoi graph (SPCoGVG) which is a semi-supervised learning framework comprised of three techniques: support vector machine (SVM), conditional random field (CRF) and generalized Voronoi graph (GVG), in order to improve the generalizability. The inherent problem of training CRF with partially labeled data has been solved using a novel parameter estimation algorithm. The effectiveness of the proposed algorithm is validated through extensive analysis of data collected in international university environments.     

Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms

A method for segmentation and recognition of image structures based on graph homomorphisms is presented in this paper. It is a model-based recognition method where the input image is over-segmented and the obtained regions are represented by an attributed relational graph (ARG). This graph is then matched against a model graph thus accomplishing the model-based recognition task. This type of problem calls for inexact graph matching through a homomorphism between the graphs since no bijective correspondence can be expected, because of the over-segmentation of the image with respect to the model. The search for the best homomorphism is carried out by optimizing an objective function based on similarities between object and relational attributes defined on the graphs. The following optimization procedures are compared and discussed: deterministic tree search, for which new algorithms are detailed, genetic algorithms and estimation of distribution algorithms. In order to assess the performance of these algorithms using real data, experimental results on supervised classification of facial features using face images from public databases are presented.