模糊支持向量机情感状态识别的研究*

针对已有的情感生理参数样本类内聚合度低、不同状态较难区分的特点,提出了一种改进的模糊支持向量机识别方法.模糊隶属度函数采用高斯分布形式,高斯分布的参数分别由同类样本数据形成的最小超球体半径和样本之间的紧密程度决定.该方法计算样本模糊隶属度时,不仅考虑样本与类中心的距离关系,还要考虑样本与样本之间的关系.实验显示改进的模...     

基于人脸图像和脑电的连续情绪识别方法

基于多模态生理数据的连续情绪识别技术在多个领域有重要用途,但碍于被试数据的缺乏和情绪的主观性,情绪识别模型的训练仍需更多的生理模态数据,且依赖于同源被试数据.本文基于人脸图像和脑电提出了多种连续情绪识别方法.在人脸图像模态,为解决人脸图像数据集少而造成的过拟合问题,本文提出了利用迁移学习技术训练的多任务卷积神经网络模型...     

Synthetic handwritten CAPTCHAs

CAPTCHAs (completely automated public Turing test to tell computers and humans apart) are in common use today as a method for performing automated human verification online. The most popular type of CAPTCHA is the text recognition variety. However, many of the existing printed text CAPTCHAs have been broken by web-bots and are hence vulnerable to attack. We present an approach to use human-like handwriting for designing CAPTCHAs. A synthetic handwriting generation method is presented, where the generated textlines need to be as close as possible to human handwriting without being writer-specific. Such handwritten CAPTCHAs exploit the differential in handwriting reading proficiency between humans and machines. Test results show that when the generated textlines are further obfuscated with a set of deformations, machine recognition rates decrease considerably, compared to prior work, while human recognition rates remain the same.     

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.

     

A Facial Expression Emotion Recognition Based Human-robot Interaction System

A facial expression emotion recognition based human-robot interaction (FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on 2D-Gabor, uniform local binary pattern (LBP) operator, and multiclass extreme learning machine (ELM) classifier is presented, which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios, i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.      

融合表情和BVP生理信号的双模态视频情感识别

目的 针对当前视频情感判别方法大多仅依赖面部表情、而忽略了面部视频中潜藏的生理信号所包含的情感信息,本文提出一种基于面部表情和血容量脉冲（BVP）生理信号的双模态视频情感识别方法。方法 首先对视频进行预处理获取面部视频;然后对面部视频分别提取LBP-TOP和HOG-TOP两种时空表情特征,并利用视频颜色放大技术获取BVP生理信号,进而提取生理信号情感特征;接着将两种特征分别送入BP分类器训练分类模型;最后利用模糊积分进行决策层融合,得出情感识别结果。结果 在实验室自建面部视频情感库上进行实验,表情单模态和生理信号单模态的平均识别率分别为80%和63.75%,而融合后的情感识别结果为83.33%,高于融合前单一模态的情感识别精度,说明了本文融合双模态进行情感识别的有效性。结论 本文提出的双模态时空特征融合的情感识别方法更能充分地利用视频中的情感信息,有效增强了视频情感的分类性能,与类似的视频情感识别算法对比实验验证了本文方法的优越性。另外,基于模糊积分的决策层融合算法有效地降低了不可靠决策信息对融合的干扰,最终获得更优的识别精度。     

Emotion extraction based on multi bio-signal using back-propagation neural network

This study proposes a system that can recognize human emotional state from bio-signal. The technology is provided to improve the interaction between humans and computers to achieve an effective human–machine that is capable for intelligent interaction. The proposed method is able to recognize six emotional states, such as joy, happiness, fear, anger, despair, and sadness. These set of emotional states are widely used for emotion recognition purposes. The result shows that the proposed method can distinguish one emotion compared to all other possible emotional states. The method is composed of two steps: 1) multi-modal bio-signal evaluation and 2) emotion recognition using artificial neural network. In the first step, we present a method to analyze and fix human sensitivity using physiological signals, such as electroencephalogram, electrocardiogram, photoplethysmogram, respiration, and galvanic skin response. The experimental analysis shows that the proposed method has good accuracy performance and could be applied on many human–computer interaction devices for emotion detection.

     

Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences.

Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypic emotional expressions, such as anger and happiness. Instead of representing another approach to machine analysis of prototypic facial expressions of emotion, the method presented in this paper attempts to handle a large range of human facial behavior by recognizing facial muscle actions that produce expressions. Virtually all of the existing vision systems for facial muscle action detection deal only with frontal-view face images and cannot handle temporal dynamics of facial actions. In this paper, we present a system for automatic recognition of facial action units (AUs) and their temporal models from long, profile-view face image sequences. We exploit particle filtering to track 15 facial points in an input face-profile sequence, and we introduce facial-action-dynamics recognition from continuous video input using temporal rules. The algorithm performs both automatic segmentation of an input video into facial expressions pictured and recognition of temporal segments (i.e., onset, apex, offset) of 27 AUs occurring alone or in a combination in the input face-profile video. A recognition rate of 87% is achieved.     

Recognizing learning emotion based on convolutional neural networks and transfer learning

Learning effectiveness is normally analyzed by data collection through tests or questionnaires. However, instant feedback is usually not available. Learners’ facial emotion and learning motivation has a positive relationship. Therefore, the system identifying learners’ facial emotions can provide feedback that teachers can understand students’ learning situation and provide help or improve teaching strategy. Studies have found that convolutional neural networks provide a good performance in basic facial emotion recognition. Convolutional neural networks do not require manual design features like traditional machine learning, they automatically learn the necessary features of the entire image. This article improves the FaceLiveNet network with low and high accuracy in basic emotion recognition, and proposes the framework of Dense_FaceLiveNet. We use Dense_FaceLiveNet for two-phases of transfer learning. First, from the relatively simple data JAFFE and KDEF basic emotion recognition model transferring to the FER2013 basic emotion dataset and obtained an accuracy of 70.02%. Secondly, using the FER2013 basic emotion recognition model transferring to learning emotion recognition model, the test accuracy rate is as high as 91.93%, which is 12.9% higher than the accuracy rate of 79.03% without using the transfer learning model, which proves that the use of transfer learning can effectively improve the recognition accuracy of learning emotion recognition model. In addition, in order to test the generalization ability of the Learning Emotion Recognition Model, videos recorded by students from a national university in Taiwan during class learning were used as test data. The original database of learning emotions did not consider that students would have exceptions such as over eyebrows, eyes closed and hand hold the chin etc. To improve this situation, after adding the learning emotion database to the images of the exceptions mentioned above, the model was rebuilt, and the recognition accuracy rate of the model was 92.42%. By comparing the output of maps, the rebuilt model does have the characteristics of success in learning images such as eyebrows, chins, and eyes closed. Furthermore, after combining all the students’ image data with the original learning emotion database, the model was rebuilt and obtained the accuracy rate reached 84.59%. The result proves that the Learning Emotion Recognition Model can achieve high recognition accuracy by processing the unlearned image through transfer learning. The main contribution is to design two-phase transfer learning for establishing the learning emotion recognition model and overcome the problem for small amounts of learning emotion data. Our experiment results have shown the performance improvement of two-phase transfer learning.     

Automatic coding of facial expressions displayed during posed and genuine pain

We present initial results from the application of an automated facial expression recognition system to spontaneous facial expressions of pain. In this study, 26 participants were videotaped under three experimental conditions: baseline, posed pain, and real pain. The real pain condition consisted of cold pressor pain induced by submerging the arm in ice water. Our goal was to (1) assess whether the automated measurements were consistent with expression measurements obtained by human experts, and (2) develop a classifier to automatically differentiate real from faked pain in a subject-independent manner from the automated measurements. We employed a machine learning approach in a two-stage system. In the first stage, a set of 20 detectors for facial actions from the Facial Action Coding System operated on the continuous video stream. These data were then passed to a second machine learning stage, in which a classifier was trained to detect the difference between expressions of real pain and fake pain. Naïve human subjects tested on the same videos were at chance for differentiating faked from real pain, obtaining only 49% accuracy. The automated system was successfully able to differentiate faked from real pain. In an analysis of 26 subjects with faked pain before real pain, the system obtained 88% correct for subject independent discrimination of real versus fake pain on a 2-alternative forced choice. Moreover, the most discriminative facial actions in the automated system were consistent with findings using human expert FACS codes.     

人机交互中的智能体情感计算研究

机器的情感是通过融入具有情感能力的智能体实现的,虽然目前在人机交互领域已经有大量研究成果,但有关智能体情感计算方面的研究尚处起步阶段,深入开展这项研究对推动人机交互领域的发展具有重要的科学和应用价值。本文通过检索Scopus数据库选择有代表性的文献,重点关注情感在智能体和用户之间的双向流动,分别从智能体对用户的情绪感知和对用户情绪调节的角度开展分析总结。首先梳理了用户情绪的识别方法,即通过用户的表情、语音、姿态、生理信号和文本信息等多通道信息分析用户的情绪状态,归纳了情绪识别中的一些机器学习方法。其次从用户体验角度分析具有情绪表现力的智能体对用户的影响,总结了智能体的情绪生成和表现技术,指出智能体除了通过表情之外,还可以通过注视、姿态、头部运动和手势等非言语动作来表现情绪。并且梳理了典型的智能体情绪架构,举例说明了强化学习在智能体情绪设计中的作用。同时为了验证模型的准确性,比较了已有的情感评估手段和评价指标。最后指出智能体情感计算急需解决的问题。通过对现有研究的总结,智能体情感计算研究是一个很有前景的研究方向,希望本文能够为深入开展相关研究提供借鉴。     

Facial expression recognition and tracking for intelligent human-robot interaction

For effective interaction between humans and socially adept, intelligent service robots, a key capability required by this class of sociable robots is the successful interpretation of visual data. In addition to crucial techniques like human face detection and recognition, an important next step for enabling intelligence and empathy within social robots is that of emotion recognition. In this paper, an automated and interactive computer vision system is investigated for human facial expression recognition and tracking based on the facial structure features and movement information. Twenty facial features are adopted since they are more informative and prominent for reducing the ambiguity during classification. An unsupervised learning algorithm, distributed locally linear embedding (DLLE), is introduced to recover the inherent properties of scattered data lying on a manifold embedded in high-dimensional input facial images. The selected person-dependent facial expression images in a video are classified using the DLLE. In addition, facial expression motion energy is introduced to describe the facial muscle’s tension during the expressions for person-independent tracking for person-independent recognition. This method takes advantage of the optical flow which tracks the feature points’ movement information. Finally, experimental results show that our approach is able to separate different expressions successfully.     

Emotion recognition from geometric facial features using self-organizing map

This paper presents a novel emotion recognition model using the system identification approach. A comprehensive data driven model using an extended Kohonen self-organizing map (KSOM) has been developed whose input is a 26 dimensional facial geometric feature vector comprising eye, lip and eyebrow feature points. The analytical face model using this 26 dimensional geometric feature vector has been effectively used to describe the facial changes due to different expressions. This paper thus includes an automated generation scheme of this geometric facial feature vector. The proposed non-heuristic model has been developed using training data from MMI facial expression database. The emotion recognition accuracy of the proposed scheme has been compared with radial basis function network, multi-layered perceptron model and support vector machine based recognition schemes. The experimental results show that the proposed model is very efficient in recognizing six basic emotions while ensuring significant increase in average classification accuracy over radial basis function and multi-layered perceptron. It also shows that the average recognition rate of the proposed method is comparatively better than multi-class support vector machine.     

Facial expression recognition based on local region specific features and support vector machines

Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.     

触觉手势情感识别的超限学习方法

为了解决声音和图像情感识别的不足,提出一种新的情感识别方式:触觉情感识别。对CoST（corpus of social touch）数据集进行了一系列触觉情感识别研究,对CoST数据集进行数据预处理,提出一些关于触觉情感识别的特征。利用极限学习机分类器探究不同手势下的情感识别,对14种手势下的3种情感（温柔、正常、暴躁）进行识别,准确度较高,且识别速度快识别时间短。结果表明,手势的不同会影响情感识别的准确率,其中手势“stroke”的识别效果在不同分类器下的分类精度均为最高,且有较好的分类精度,达到72.07%;极限学习机作为触觉情感识别的分类器,具有较好的分类效果,识别速度快;有的手势本身对应着某种情感,从而影响分类结果。     

基于几何特征及C4.5的人脸美丽分类方法

从机器学习的角度来探索人脸美,提出与中国女性美丽程度相关的17维特征提取方法,然后运用C4。5分类树对不同美丽评分的人脸图像进行训练和测试。对510幅中国女性人脸图像的实验结果表明,文中提出的人脸美丽评价方法简单可行。对于美丽与否的两类别,平均分类精度达到94。1%。而对于4种美丽等级的分类,可达到71。6%的精度。研究表明通过合适的特征及C4。5机器学习来进行人脸美丽的智能感知是可行的。     

TC-Net: A Modest & Lightweight Emotion Recognition System Using Temporal Convolution Network

Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines. Speech Emotion Recognition (SER) is one of the critical sources for human evaluation, which is applicable in many real-world applications such as healthcare, call centers, robotics, safety, and virtual reality. This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state. The authors designed a Temporal Convolutional Network (TCN) core block to recognize long-term dependencies in speech signals and then feed these temporal cues to a dense network to fuse the spatial features and recognize global information for final classification. The proposed network extracts valid sequential cues automatically from speech signals, which performed better than state-of-the-art (SOTA) and traditional machine learning algorithms. Results of the proposed method show a high recognition rate compared with SOTA methods. The final unweighted accuracy of 80.84%, and 92.31%, for interactive emotional dyadic motion captures (IEMOCAP) and berlin emotional dataset (EMO-DB), indicate the robustness and efficiency of the designed model.     

State-of-the art in component technology for an animated face robot-its component technology development for interactive communication with humans

We are attempting to introduce a 3D, realistic human-like animated face robot to human-robot communication. The face robot can recognize human facial expressions as well as produce realistic facial expressions in real time. For the animated face robot to communicate interactively, we propose a new concept of 'active human interface', and we investigate the performance of real time recognition of facial expressions by neural networks (NN) and the expressionability of facial messages on the face robot. We find that the NN recognition of facial expressions and the face robot's performance in generating facial expressions are of almost same level as that in humans. We also construct an artificial emotion model able to generate six basic emotions in accordance with the recognition of a given facial expression and the situational context. This implies a high potential for the animated face robot to undertake interactive communication with humans, when integrating these three component technologies into the face robot.     

Evolving an emotion recognition module for an intelligent agent using genetic programming and a genetic algorithm

Most studies use the facial expression to recognize a user’s emotion; however, gestures, such as nodding, shaking the head, or stillness can also be indicators of the user’s emotion. In our research, we use the facial expression and gestures to detect and recognize a user’s emotion. The pervasive Microsoft Kinect sensor captures video data, from which several features representing facial expressions and gestures are extracted. An in-house extensible markup language-based genetic programming engine (XGP) evolves the emotion recognition module of our system. To improve the computational performance of the recognition module, we implemented and compared several approaches, including directed evolution, collaborative filtering via canonical voting, and a genetic algorithm, for an automated voting system. The experimental results indicate that XGP is feasible for evolving emotion classifiers. In addition, the obtained results verify that collaborative filtering improves the generality of recognition. From a psychological viewpoint, the results prove that different people might express their emotions differently, as the emotion classifiers that are evolved for particular users might not be applied successfully to other user(s).