The human face constantly conveys information, both consciously and subconsciously. However, as basic as it is for humans to visually interpret this information, it is quite a big challenge for machines. Conventional semantic facial feature recognition and analysis techniques are already in use and are based on physiological heuristics, but they suffer from lack of robustness and high computation time. This thesis aims to explore ways for machines to learn to interpret semantic information available in faces in an automated manner without requiring manual design of feature detectors, using the approach of Deep Learning. This thesis provides a study of the effects of various factors and hyper-parameters of deep neural networks in the process of determining an optimal network configuration for the task of semantic facial feature recognition. This thesis explores the effectiveness of the system to recognize the various semantic features (like emotions, age, gender, ethnicity etc.) present in faces. Furthermore, the relation between the effect of high-level concepts on low level features is explored through an analysis of the similarities in low-level descriptors of different semantic features. This thesis also demonstrates a novel idea of using a deep network to generate 3-D Active Appearance Models of faces from real-world 2-D images. For a more detailed report on this work, please see [arXiv:1512.00743v1].
In the task of emotion recognition from faces, Tang's @cite_22 sets the state-of-the-art on the Facial Expression Recognition Challenge (FERC) dataset. This is achieved by implementing a two stage network: a convolutional network trained in a supervised manner on the first stage, and a Support Vector Machine as the second stage trained on the output of the first stage. Recent work by in @cite_12 successfully demonstrates a multi-modal deep learning based framework for emotion recognition in videos.
@cite_22 @cite_12
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In this paper we present the techniques used for the University of Montreal's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58 . Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03 on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56 . Abstract In this paper, an eleven-layered Convolutional Neural Network with Visual Attention is proposed for facial expression recognition. The network is composed of three components. First, local convolutional features of faces are extracted by a stack of ten convolutional layers. Second, the regions of interest are automatically determined according to these local features by the embedded attention model. Third, the local features in these regions are aggregated and used to infer the emotional label. These three components are integrated into a single network which can be trained in an end-to-end scheme. Extensive experiments on four kinds of data (namely aligned frontal faces, faces in different poses, aligned unconstrained faces, and grouped unconstrained faces) prove that the proposed method can improve the accuracy and obtain good visualization. The visualization shows that the learned regions of interest are partly consistent with the locations of emotion specific Action Units. This founding confirms the interpretation of Facial Action Coding System and Emotional Facial Action Coding System from a machine learning perspective. We report our image based static facial expression recognition method for the Emotion Recognition in the Wild Challenge (EmotiW) 2015. We focus on the sub-challenge of the SFEW 2.0 dataset, where one seeks to automatically classify a set of static images into 7 basic emotions. The proposed method contains a face detection module based on the ensemble of three state-of-the-art face detectors, followed by a classification module with the ensemble of multiple deep convolutional neural networks (CNN). Each CNN model is initialized randomly and pre-trained on a larger dataset provided by the Facial Expression Recognition (FER) Challenge 2013. The pre-trained models are then fine-tuned on the training set of SFEW 2.0. To combine multiple CNN models, we present two schemes for learning the ensemble weights of the network responses: by minimizing the log likelihood loss, and by minimizing the hinge loss. Our proposed method generates state-of-the-art result on the FER dataset. It also achieves 55.96 and 61.29 respectively on the validation and test set of SFEW 2.0, surpassing the challenge baseline of 35.96 and 39.13 with significant gains. This paper presents the techniques employed in our team's submissions to the 2015 Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression Recognition in the Wild. The objective of this sub-challenge is to classify the emotions expressed by the primary human subject in static images extracted from movies. We follow a transfer learning approach for deep Convolutional Neural Network (CNN) architectures. Starting from a network pre-trained on the generic ImageNet dataset, we perform supervised fine-tuning on the network in a two-stage process, first on datasets relevant to facial expressions, followed by the contest's dataset. Experimental results show that this cascading fine-tuning approach achieves better results, compared to a single stage fine-tuning with the combined datasets. Our best submission exhibited an overall accuracy of 48.5 in the validation set and 55.6 in the test set, which compares favorably to the respective 35.96 and 39.13 of the challenge baseline. Facial expression recognition is a topical task. However, very little research investigates subtle expression recognition, which is important for mental activity analysis, deception detection, etc. We address subtle expression recognition through convolutional neural networks (CNNs) by developing multi-task learning (MTL) methods to effectively leverage a side task: facial landmark detection. Existing MTL methods follow a design pattern of shared bottom CNN layers and task-specific top layers. However, the sharing architecture is usually heuristically chosen, as it is difficult to decide which layers should be shared. Our approach is composed of (1) a novel MTL framework that automatically learns which layers to share through optimisation under tensor trace norm regularisation and (2) an invariant representation learning approach that allows the CNN to leverage tasks defined on disjoint datasets without suffering from dataset distribution shift. To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW). LSEMSW includes a variety of cognitive states as well as basic emotions. It contains 176K images, manually annotated with 13 emotions, and thus provides the first subtle expression dataset large enough for training deep CNNs. Evaluations on LSEMSW and 300-W (landmark) databases show the effectiveness of the proposed methods. In addition, we investigate transferring knowledge learned from LSEMSW database to traditional (non-subtle) expression recognition. We achieve very competitive performance on Oulu-Casia NIR&Vis and CK+ databases via transfer learning. We propose a semi-supervised approach to solve the task of emotion recognition in 2D face images using recent ideas in deep learning for handling the factors of variation present in data. An emotion classification algorithm should be both robust to (1) remaining variations due to the pose of the face in the image after centering and alignment, (2) the identity or morphology of the face. In order to achieve this invariance, we propose to learn a hierarchy of features in which we gradually filter the factors of variation arising from both (1) and (2). We address (1) by using a multi-scale contractive convolutional network (CCNET) in order to obtain invariance to translations of the facial traits in the image. Using the feature representation produced by the CCNET, we train a Contractive Discriminative Analysis (CDA) feature extractor, a novel variant of the Contractive Auto-Encoder (CAE), designed to learn a representation separating out the emotion-related factors from the others (which mostly capture the subject identity, and what is left of pose after the CCNET). This system beats the state-of-the-art on a recently proposed dataset for facial expression recognition, the Toronto Face Database, moving the state-of-art accuracy from 82.4 to 85.0 , while the CCNET and CDA improve accuracy of a standard CAE by 8 . This paper presents our work in the FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) and we participate in the AU occurrence sub-challenge. Our work of AU occurrence recognition is based on deep learning, and we design convolution neural network (CNN) models for two types of work: facial view recognition and AU occurrence recognition. For facial view recognition, our model could achieve 97.7 accuracy on validation dataset about 9 facial views. For AU occurrence recognition, we use both visual features and temporal information of dataset. We use CNN models to get deep visual feature and then use BLSTM-RNN to learn the high-level feature in the time domain. When training models, we divide dataset into 9 parts based on 9 facial views, and each model is trained in a specific view. When recognizing AUs, we recognize facial view first and then choose the corresponding model for AU occurrence recognition. Finally, our method shows good performance, the F1 score of test data is 0.507 and the accuracy is 0.735. We have developed a convolutional neural network for the purpose of recognizing facial expressions in human beings. We have fine-tuned the existing convolutional neural network model trained on the visual recognition dataset used in the ILSVRC2012 to two widely used facial expression datasets - CFEE and RaFD, which when trained and tested independently yielded test accuracies of 74.79 and 95.71 , respectively. Generalization of results was evident by training on one dataset and testing on the other. Further, the image product of the cropped faces and their visual saliency maps were computed using Deep Multi-Layer Network for saliency prediction and were fed to the facial expression recognition CNN. In the most generalized experiment, we observed the top-1 accuracy in the test set to be 65.39 . General confusion trends between different facial expressions as exhibited by humans were also observed. In this paper, we present HoloNet, a well-designed Convolutional Neural Network (CNN) architecture regarding our submissions to the video based sub-challenge of the Emotion Recognition in the Wild (EmotiW) 2016 challenge. In contrast to previous related methods that usually adopt relatively simple and shallow neural network architectures to address emotion recognition task, our HoloNet has three critical considerations in network design. (1) To reduce redundant filters and enhance the non-saturated non-linearity in the lower convolutional layers, we use a modified Concatenated Rectified Linear Unit (CReLU) instead of ReLU. (2) To enjoy the accuracy gain from considerably increased network depth and maintain efficiency, we combine residual structure and CReLU to construct the middle layers. (3) To broaden network width and introduce multi-scale feature extraction property, the topper layers are designed as a variant of inception-residual structure. The main benefit of grouping these modules into the HoloNet is that both negative and positive phase information implicitly contained in the input data can flow over it in multiple paths, thus deep multi-scale features explicitly capturing emotion variation can be well extracted from multi-path sibling layers, and then can be further concatenated for robust recognition. We obtain competitive results in this year’s video based emotion recognition sub-challenge using an ensemble of two HoloNet models trained with given data only. Specifically, we obtain a mean recognition rate of 57.84 , outperforming the baseline accuracy with an absolute margin of 17.37 , and yielding 4.04 absolute accuracy gain compared to the result of last year’s winner team. Meanwhile, our method runs with a speed of several thousands of frames per second on a GPU, thus it is well applicable to real-time scenarios. In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has drawn more and more attention due to its huge potential applications. In the fourth challenge, aimed at the task of video based emotion recognition, we propose a multi-clue emotion fusion (MCEF) framework by modeling human emotion from three mutually complementary sources, facial appearance texture, facial action, and audio. To extract high-level emotion features from sequential face images, we employ a CNN-RNN architecture, where face image from each frame is first fed into the fine-tuned VGG-Face network to extract face feature, and then the features of all frames are sequentially traversed in a bidirectional RNN so as to capture dynamic changes of facial textures. To attain more accurate facial actions, a facial landmark trajectory model is proposed to explicitly learn emotion variations of facial components. Further, audio signals are also modeled in a CNN framework by extracting low-level energy features from segmented audio clips and then stacking them as an image-like map. Finally, we fuse the results generated from three clues to boost the performance of emotion recognition. Our proposed MCEF achieves an overall accuracy of 56.66 with a large improvement of 16.19 with respect to the baseline. State-of-the-art approaches for the previous emotion recognition in the wild challenges are usually built on prevailing Convolutional Neural Networks (CNNs). Although there is clear evidence that CNNs with increased depth or width can usually bring improved predication accuracy, existing top approaches provide supervision only at the output feature layer, resulting in the insufficient training of deep CNN models. In this paper, we present a new learning method named Supervised Scoring Ensemble (SSE) for advancing this challenge with deep CNNs. We first extend the idea of recent deep supervision to deal with emotion recognition problem. Benefiting from adding supervision not only to deep layers but also to intermediate layers and shallow layers, the training of deep CNNs can be well eased. Second, we present a new fusion structure in which class-wise scoring activations at diverse complementary feature layers are concatenated and further used as the inputs for second-level supervision, acting as a deep feature ensemble within a single CNN architecture. We show our proposed learning method brings large accuracy gains over diverse backbone networks consistently. On this year's audio-video based emotion recognition task, the average recognition rate of our best submission is 60.34 , forming a new envelop over all existing records. This paper presents our proposed approach for the second Emotion Recognition in The Wild Challenge. We propose a new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes (HOG_TOP) to represent facial expressions. We also explore the properties of visual features and audio features, and adopt Multiple Kernel Learning (MKL) to find an optimal feature fusion. An SVM with multiple kernels is trained for the facial expression classification. Experimental results demonstrate that our method achieves a promising performance. The overall classification accuracy on the validation set and test set are 40.21 and 45.21 , respectively. In this work we report on the progress of building a system that enables fully automated fast and robust facial expression recognition from face video. We analyse subtle changes in facial expression by recognizing facial muscle action units (AUs) and analysing their temporal behavior. By detecting AUs from face video we enable the analysis of various facial communicative signals including facial expressions of emotion, attitude and mood. For an input video picturing a facial expression we detect per frame whether any of 15 different AUs is activated, whether that facial action is in the onset, apex, or offset phase, and what the total duration of the activation in question is. We base this process upon a set of spatio-temporal features calculated from tracking data for 20 facial fiducial points. To detect these 20 points of interest in the first frame of an input face video, we utilize a fully automatic, facial point localization method that uses individual feature GentleBoost templates built from Gabor wavelet features. Then, we exploit a particle filtering scheme that uses factorized likelihoods and a novel observation model that combines a rigid and a morphological model to track the facial points. The AUs displayed in the input video and their temporal segments are recognized finally by Support Vector Machines trained on a subset of most informative spatio-temporal features selected by AdaBoost. For Cohn-Kanade andMMI databases, the proposed system classifies 15 AUs occurring alone or in combination with other AUs with a mean agreement rate of 90.2 with human FACS coders. In this paper we address the sentence-level multi-modal emotion recognition problem. We formulate the emotion recognition task as a multi-category classification problem and propose an innovative solution based on the automatically generated ensemble of trees with binary support vector machines (SVM) classifiers in the tree nodes. We demonstrate the efficacy of our approach by performing four-way (anger, happiness, sadness, neutral) and five-way (including excitement) emotion recognition on the University of Southern California's Interactive Emotional Motion Capture (USC-IEMOCAP) corpus using combinations of acoustic features, lexical features extracted from automatic speech recognition (ASR) output and visual features extracted from facial markers traced by a motion capture system. The experiments show that the proposed ensemble of trees of binary SVM classifiers outperforms classical multi-way SVM classification with one-vs-one voting scheme and achieves state-of-the-art results for all feature combinations. Facial expression recognition has been investigated for many years, and there are two popular models: Action Units (AUs) and the Valence-Arousal space (V-A space) that have been widely used. However, most of the databases for estimating V-A intensity are captured in laboratory settings, and the benchmarks "in-the-wild" do not exist. Thus, the First Affect-In-The-Wild Challenge released a database for V-A estimation while the videos were captured in wild condition. In this paper, we propose an integrated deep learning framework for facial attribute recognition, AU detection, and V-A estimation. The key idea is to apply AUs to estimate the V-A intensity since both AUs and V-A space could be utilized to recognize some emotion categories. Besides, the AU detector is trained based on the convolutional neural network (CNN) for facial attribute recognition. In experiments, we will show the results of the above three tasks to verify the performances of our proposed network framework. 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. HighlightsWe propose an emotion recognition model using system identification.Twenty six dimensional geometric feature vector is extracted using three different algorithms.Classification using an intermediate Kohonen self-organizing map layer.A comparative study with Radial basis function, Multi-layer perceptron and Support vector machine.Efficient recognition results with significant increase in average recognition accuracy over radial basis function and multi-layer perceptron. Marginal improvement over support vector machine. This paper presents our contribution to ACM ICMI 2015 Emotion Recognition in the Wild Challenge (EmotiW 2015). We participate in both static facial expression (SFEW) and audio-visual emotion recognition challenges. In both challenges, we use a set of visual descriptors and their early and late fusion schemes. For AFEW, we also exploit a set of popularly used spatio-temporal modeling alternatives and carry out multi-modal fusion. For classification, we employ two least squares regression based learners that are shown to be fast and accurate on former EmotiW Challenge corpora. Specifically, we use Partial Least Squares Regression (PLS) and Kernel Extreme Learning Machines (ELM), which is closely related to Kernel Regularized Least Squares. We use a General Procrustes Analysis (GPA) based alignment for face registration. By employing different alignments, descriptor types, video modeling strategies and classifiers, we diversify learners to improve the final fusion performance. Test set accuracies reached in both challenges are relatively 25 above the respective baselines. We present a pattern recognition framework to improve committee machines of deep convolutional neural networks (deep CNNs) and its application to static facial expression recognition in the wild (SFEW). In order to generate enough diversity of decisions, we trained multiple deep CNNs by varying network architectures, input normalization, and weight initialization as well as by adopting several learning strategies to use large external databases. Moreover, with these deep models, we formed hierarchical committees using the validation-accuracy-based exponentially-weighted average (VA-Expo-WA) rule. Through extensive experiments, the great strengths of our committee machines were demonstrated in both structural and decisional ways. On the SFEW2.0 dataset released for the 3rd Emotion Recognition in the Wild (EmotiW) sub-challenge, a test accuracy of 57.3 was obtained from the best single deep CNN, while the single-level committees yielded 58.3 and 60.5 with the simple average rule and with the VA-Expo-WA rule, respectively. Our final submission based on the 3-level hierarchy using the VA-Expo-WA achieved 61.6 , significantly higher than the SFEW baseline of 39.1 . This paper exploits the discriminative power of manifold learning in conjunction with the parsimonious power of sparse signal representation to perform robust facial expression recognition. By utilizing an l1 reconstruction error and a statistical mixture model, both accuracy and tolerance to occlusion improve without the need to perform neutral frame subtraction. Initially facial features are mapped onto a low dimensional manifold using supervised Locality Preserving Projections. Then an l1 optimization is employed to relate surface projections to training exemplars, where reconstruction models on facial regions determine the expression class. Experimental procedures and results are done in accordance with the recently published extended Cohn-Kanade and GEMEP-FERA datasets. Results demonstrate that posed datasets overemphasize the mouth region, while spontaneous datasets rely more on the upper cheek and eye regions. Despite these differences, the proposed method overcomes previous limitations to using sparse methods for facial expression and produces state-of-the-art results on both types of datasets. In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017. We propose an ensemble of several models, which capture spatial and audio features from videos. Spatial features are captured by convolutional neural networks, pretrained on large face recognition datasets. We show that usage of strong industry-level face recognition networks increases the accuracy of emotion recognition. Using our ensemble we improve on the previous best result on the test set by about 1 , achieving a 60.03 classification accuracy without any use of visual temporal information.