Recognising facial expressions in video sequences

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated with facial expressions is represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold to compute a posterior probability associated with a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89% recognition rate in a set of 333 sequences from the Cohn–Kanade database.     

Natural facial expression recognition using differential-AAM and manifold learning

This paper proposes a novel natural facial expression recognition method that recognizes a sequence of dynamic facial expression images using the differential active appearance model (AAM) and manifold learning as follows. First, the differential-AAM features (DAFs) are computed by the difference of the AAM parameters between an input face image and a reference (neutral expression) face image. Second, manifold learning embeds the DAFs on the smooth and continuous feature space. Third, the input facial expression is recognized through two steps: (1) computing the distances between the input image sequence and gallery image sequences using directed Hausdorff distance (DHD) and (2) selecting the expression by a majority voting of k-nearest neighbors (k-NN) sequences in the gallery. The DAFs are robust and efficient for the facial expression analysis due to the elimination of the inter-person, camera, and illumination variations. Since the DAFs treat the neutral expression image as the reference image, the neutral expression image must be found effectively. This is done via the differential facial expression probability density model (DFEPDM) using the kernel density approximation of the positively directional DAFs changing from neutral to angry (happy, surprised) and negatively directional DAFs changing from angry (happy, surprised) to neutral. Then, a face image is considered to be the neutral expression if it has the maximum DFEPDM in the input sequences. Experimental results show that (1) the DAFs improve the facial expression recognition performance over conventional AAM features by 20% and (2) the sequence-based k-NN classifier provides a 95% facial expression recognition performance on the facial expression database (FED06).     

Feature representation for facial expression recognition based on FACS and LBP

In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system(FACS) and "uniform" local binary patterns(LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models(ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood(K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience.     

一种基于降噪自编码器的人脸表情识别方法

针对传统人脸表情识别算法鲁棒性差,易受到人脸身份信息干扰的问题,本文在基于降噪自编码器的基础上,提出一种人脸表情识别算法。首先,从图片中检测出人脸部分,并进行尺度归一化处理;再构造堆栈式降噪自编码神经网络模型进行预训练;最后为了避免由训练样本不足容易造成的过拟合问题,在深度网络模型的全连接层采用了Dropout技术。实验结果在数据集CK 、JAFFE和Yale上均取得了较高的准确率,说明了该方法具有较强的鲁棒性和抗身份信息干扰的能力。     

Non-manual grammatical marker recognition based on multi-scale,spatio-temporal analysis of head pose and facial expressions

Changes in eyebrow configuration, in conjunction with other facial expressions and head gestures, are used to signal essential grammatical information in signed languages. This paper proposes an automatic recognition system for non-manual grammatical markers in American Sign Language (ASL) based on a multi-scale, spatio-temporal analysis of head pose and facial expressions. The analysis takes account of gestural components of these markers, such as raised or lowered eyebrows and different types of periodic head movements. To advance the state of the art in non-manual grammatical marker recognition, we propose a novel multi-scale learning approach that exploits spatio-temporally low-level and high-level facial features. Low-level features are based on information about facial geometry and appearance, as well as head pose, and are obtained through accurate 3D deformable model-based face tracking. High-level features are based on the identification of gestural events, of varying duration, that constitute the components of linguistic non-manual markers. Specifically, we recognize events such as raised and lowered eyebrows, head nods, and head shakes. We also partition these events into temporal phases. We separate the anticipatory transitional movement (the onset) from the linguistically significant portion of the event, and we further separate the core of the event from the transitional movement that occurs as the articulators return to the neutral position towards the end of the event (the offset). This partitioning is essential for the temporally accurate localization of the grammatical markers, which could not be achieved at this level of precision with previous computer vision methods. In addition, we analyze and use the motion patterns of these non-manual events. Those patterns, together with the information about the type of event and its temporal phases, are defined as the high-level features. Using this multi-scale, spatio-temporal combination of low- and high-level features, we employ learning methods for accurate recognition of non-manual grammatical markers in ASL sentences.     

遮挡人脸表情识别深度学习方法研究进展

在真实环境下遮挡是准确分析识别人脸表情的主要障碍之一。近年来研究者采用深度学习技术解决遮挡条件下表情误识别率高的问题。针对遮挡表情识别的深度学习算法和遮挡相关的问题进行归纳总结。首先,概括局部遮挡条件下表情识别的发展现状、表情的表示方式以及研究遮挡表情用到的数据集;其次,回顾遮挡表情识别深度学习方法的最新进展和分析遮挡对表情的影响;最后,总结主要技术挑战,研究难点及其可能的应对策略。目的是为将来的遮挡表情识别研究提供更有益的参考依据和基准。     

深度多尺度融合注意力残差人脸表情识别网络

针对人脸表情呈现方式多样化以及人脸表情识别易受光照、姿势、遮挡等非线性因素影响的问题,提出了一种深度多尺度融合注意力残差网络(deep multi-scale fusion attention residual network, DMFA-ResNet)。该模型基于ResNet-50残差网络,设计了新的注意力残差模块,由7个具有三条支路的注意残差学习单元构成,能够对输入图像进行并行多卷积操作,以获得多尺度特征,同时引入注意力机制,突出重点局部区域,有利于遮挡图像的特征学习。通过在注意力残差模块之间增加过渡层以去除冗余信息,简化网络复杂度,在保证感受野的情况下减少计算量,实现网络抗过拟合效果。在3组数据集上的实验结果表明,本文提出的算法均优于对比的其他先进方法。     

Facial expression and body gesture emotion recognition: A systematic review on the use of visual data in affective computing

Emotion is an important driver of human decision-making and communication. With the recent rise of human–computer interaction, affective computing has become a trending research topic, aiming to develop computational systems that can understand human emotions and respond to them. A systematic review has been conducted to fill these gaps since previous reviews regarding machine-enabled automated visual emotion recognition neglect important methodological aspects, including emotion models and hardware usage. 467 relevant papers were initially found and examined. After the screening process with specific inclusion and exclusion criteria, 30 papers were selected. Methodological aspects including emotion models, devices, architectures, and classification techniques employed by the selected studies were analyzed, and the most popular techniques and current trends in visual emotion recognition were identified. This review not only offers a comprehensive and up-to-date overview of the topic but also provides researchers with insights regarding methodological aspects like emotion models employed, devices used, and classification techniques for automated visual emotion recognition. By identifying current trends, like the increased use of deep learning algorithms and the need for further study on body gestures, this review advocates the advantages of implementing emotion recognition with the use of visual data and builds a solid foundation for applying relevant techniques in different fields.     

Dempster-Shafer证据融合金字塔韦伯局部特征的表情识别

目的 针对韦伯局部特征(WLD)在局部细节表征上的局限性,提出一种基于金字塔韦伯局部特征(PWLD)的人脸表情识别方法。方法 首先对人脸图像进行预处理,分割出表情显著区域;然后根据显著区域大小进行分层并加以分块,对每一层的不同块提取PWLD特征,将测试集与训练集的PWLD直方图的卡方距离归一化作为独立证据来构造基本概率分配(BPA);最后利用D-S(Dempster-Shafer)证据理论联合规则得到融合后的BPA,进而得出识别结果。结果 在JAFFE库与Cohn-Kanade库上进行交叉验证,平均识别率分别为95.81%和97.47%。结论 PWLD特征在局部细节表征上比WLD特征更具有优势。与目前典型的方法进行对比实验,验证该方法的有效性和容错性,同时证明该方法在噪声和光照条件下具有一定的鲁棒性。