FusionSense: Emotion Classification using Feature Fusion of Multimodal Data and Deep learning in a Brain-inspired Spiking Neural Network

Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.Peer reviewe

Semi-supervised Emotion Recognition using Inconsistently Annotated Data

International audienceExpression recognition remains challenging, predominantly due to (a) lack of sufficient data, (b) subtle emotion intensity, (c) subjective and inconsistent annotation, as well as due to (d) in-the-wild data containing variations in pose, intensity, and occlusion. To address such challenges in a unified framework, we propose a self-training based semi-supervised convolutional neural network (CNN) framework, which directly addresses the problem of (a) limited data by leveraging information from unannotated samples. Our method uses 'successive label smoothing' to adapt to the subtle expressions and improve the model performance for (b) low-intensity expression samples. Further, we address (c) inconsistent annotations by assigning sample weights during loss computation, thereby ignoring the effect of incorrect ground-truth. We observe significant performance improvement in in-the-wild datasets by leveraging the information from the in-the-lab datasets, related to challenge (d). Associated to that, experiments on four publicly available datasets demonstrate large performance gains in cross-database performance, as well as show that the proposed method achieves to learn different expression intensities, even when trained with categorical samples

Artificial Intelligence Tools for Facial Expression Analysis.

Inner emotions show visibly upon the human face and are understood as a basic guide to an individual’s inner world. It is, therefore, possible to determine a person’s attitudes and the effects of others’ behaviour on their deeper feelings through examining facial expressions. In real world applications, machines that interact with people need strong facial expression recognition. This recognition is seen to hold advantages for varied applications in affective computing, advanced human-computer interaction, security, stress and depression analysis, robotic systems, and machine learning. This thesis starts by proposing a benchmark of dynamic versus static methods for facial Action Unit (AU) detection. AU activation is a set of local individual facial muscle parts that occur in unison constituting a natural facial expression event. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. For this research, AU occurrence activation detection was conducted by extracting features (static and dynamic) of both nominal hand-crafted and deep learning representation from each static image of a video. This confirmed the superior ability of a pretrained model that leaps in performance. Next, temporal modelling was investigated to detect the underlying temporal variation phases using supervised and unsupervised methods from dynamic sequences. During these processes, the importance of stacking dynamic on top of static was discovered in encoding deep features for learning temporal information when combining the spatial and temporal schemes simultaneously. Also, this study found that fusing both temporal and temporal features will give more long term temporal pattern information. Moreover, we hypothesised that using an unsupervised method would enable the leaching of invariant information from dynamic textures. Recently, fresh cutting-edge developments have been created by approaches based on Generative Adversarial Networks (GANs). In the second section of this thesis, we propose a model based on the adoption of an unsupervised DCGAN for the facial features’ extraction and classification to achieve the following: the creation of facial expression images under different arbitrary poses (frontal, multi-view, and in the wild), and the recognition of emotion categories and AUs, in an attempt to resolve the problem of recognising the static seven classes of emotion in the wild. Thorough experimentation with the proposed cross-database performance demonstrates that this approach can improve the generalization results. Additionally, we showed that the features learnt by the DCGAN process are poorly suited to encoding facial expressions when observed under multiple views, or when trained from a limited number of positive examples. Finally, this research focuses on disentangling identity from expression for facial expression recognition. A novel technique was implemented for emotion recognition from a single monocular image. A large-scale dataset (Face vid) was created from facial image videos which were rich in variations and distribution of facial dynamics, appearance, identities, expressions, and 3D poses. This dataset was used to train a DCNN (ResNet) to regress the expression parameters from a 3D Morphable Model jointly with a back-end classifier

3D Face Modelling, Analysis and Synthesis

Human faces have always been of a special interest to researchers in the computer vision and graphics areas. There has been an explosion in the number of studies around accurately modelling, analysing and synthesising realistic faces for various applications. The importance of human faces emerges from the fact that they are invaluable means of effective communication, recognition, behaviour analysis, conveying emotions, etc. Therefore, addressing the automatic visual perception of human faces efficiently could open up many influential applications in various domains, e.g. virtual/augmented reality, computer-aided surgeries, security and surveillance, entertainment, and many more. However, the vast variability associated with the geometry and appearance of human faces captured in unconstrained videos and images renders their automatic analysis and understanding very challenging even today. The primary objective of this thesis is to develop novel methodologies of 3D computer vision for human faces that go beyond the state of the art and achieve unprecedented quality and robustness. In more detail, this thesis advances the state of the art in 3D facial shape reconstruction and tracking, fine-grained 3D facial motion estimation, expression recognition and facial synthesis with the aid of 3D face modelling. We give a special attention to the case where the input comes from monocular imagery data captured under uncontrolled settings, a.k.a. \textit{in-the-wild} data. This kind of data are available in abundance nowadays on the internet. Analysing these data pushes the boundaries of currently available computer vision algorithms and opens up many new crucial applications in the industry. We define the four targeted vision problems (3D facial reconstruction $\&$ tracking, fine-grained 3D facial motion estimation, expression recognition, facial synthesis) in this thesis as the four 3D-based essential systems for the automatic facial behaviour understanding and show how they rely on each other. Finally, to aid the research conducted in this thesis, we collect and annotate a large-scale videos dataset of monocular facial performances. All of our proposed methods demonstarte very promising quantitative and qualitative results when compared to the state-of-the-art methods

Expression Recognition with Deep Features Extracted from Holistic and Part-based Models

International audienceFacial expression recognition aims to accurately interpret facial muscle movements in affective states (emotions). Previous studies have proposed holistic analysis of the face, as well as the extraction of features pertained only to specific facial regions towards expression recognition. While classically the latter have shown better performances, we here explore this in the context of deep learning. In particular, this work provides a performance comparison of holistic and part-based deep learning models for expression recognition. In addition, we showcase the effectiveness of skip connections, which allow a network to infer from both low and high-level feature maps. Our results suggest that holistic models outperform part-based models, in the absence of skip connections. Finally, based on our findings, we propose a data augmentation scheme, which we incorporate in a part-based model. The proposed multi-face multi-part (MFMP) model leverages the wide information from part-based data augmentation, where we train the network using the facial parts extracted from different face samples of the same expression class. Extensive experiments on publicly available datasets show a significant improvement of facial expression classification with the proposed MFMP framework

Deep Recurrent Learning for Efficient Image Recognition Using Small Data

Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data for successful learning. Additionally, the feedforward-based hierarchical models do not exploit another important biological learning paradigm, known as recurrency, which ubiquitously exists in the biological visual system and has been shown to be quite crucial for recognition. Consequently, this work aims to develop novel biologically relevant deep recurrent learning models for robust recognition using limited training data. First, we design an efficient deep simultaneous recurrent network (DSRN) architecture for solving several challenging image recognition tasks. The use of simultaneous recurrency in the proposed model improves the recognition performance and offers reduced computational complexity compared to the existing hierarchical deep learning models. Moreover, the DSRN architecture inherently learns meaningful representations of data during the training process which is essential to achieve superior recognition performance. However, probabilistic models such as deep generative models are particularly adept at learning representations directly from unlabeled input data. Accordingly, we show the generalization of the proposed deep simultaneous recurrency concept by developing a probabilistic deep simultaneous recurrent belief network (DSRBN) architecture which is more efficient in learning the underlying representation of the data compared to the state-of-the-art generative models. Finally, we propose a deep recurrent learning framework for solving the image recognition task using small data. We incorporate Bayesian statistics to the DSRBN generative model to propose a deep recurrent generative Bayesian model that addresses the challenge of learning from a small amount of data. Our findings suggest that the proposed deep recurrent Bayesian framework demonstrates better image recognition performance compared to the state-of-the-art models in a small data learning scenario. In conclusion, this dissertation proposes novel deep recurrent learning pipelines, which utilize not only limited training data to achieve improved image recognition performance but also require significantly reduced training parameters

Reconnaissance des expressions faciales pour l’assistance ambiante

Au cours de ces dernières décennies, le monde a connu d’importants changements démographiques et notamment au niveau de la population âgée qui a fortement augmenté. La prise d’âge a comme conséquence directe non seulement une perte progressive des facultés cognitives, mais aussi un risque plus élevé d’être atteint de maladies neurodégénératives telles qu’Alzheimer et Parkinson. La perte des facultés cognitives cause une diminution de l’autonomie et par conséquent, une assistance quotidienne doit être fournie à ces individus afin d’assurer leur bien-être. Les établissements ainsi que le personnel spécialisé censés les prendre en charge représentent un lourd fardeau pour l’économie. Pour cette raison, d’autres solutions moins coûteuses et plus optimisées doivent être proposées. Avec l’avènement des nouvelles technologies de l’information et de la communication, il est devenu de plus en plus aisé de développer des solutions permettant de fournir une assistance adéquate aux personnes souffrant de déficiences cognitives. Les maisons intelligentes représentent l’une des solutions les plus répandues. Elles exploitent différents types de capteurs pour la collecte de données, des algorithmes et méthodes d’apprentissage automatique pour l’extraction/traitement de l’information et des actionneurs pour le déclenchement d’une réponse fournissant une assistance adéquate. Parmi les différentes sources de données qui sont exploitées, les images/vidéos restent les plus riches en termes de quantité. Les données récoltées permettent non seulement la reconnaissance d’activités, mais aussi la détection d’erreur durant l’exécution de tâches/activités de la vie quotidienne. La reconnaissance automatique des émotions trouve de nombreuses applications dans notre vie quotidienne telles que l’interaction homme-machine, l’éducation, la sécurité, le divertissement, la vision robotique et l’assistance ambiante. Cependant, les émotions restent un sujet assez complexe à cerner et de nombreuses études en psychologie et sciences cognitives continuent d’être effectuées. Les résultats obtenus servent de base afin de développer des approches plus efficaces. Les émotions humaines peuvent être perçues à travers différentes modalités telle que la voix, la posture, la gestuelle et les expressions faciales. En se basant sur les travaux de Mehrabian, les expressions faciales représentent la modalité la plus pertinente pour la reconnaissance automatique des émotions. Ainsi, l’un des objectifs de ce travail de recherche consistera à proposer des méthodes permettant l’identification des six émotions de base à savoir : la joie, la peur, la colère, la surprise, le dégoût et la tristesse. Les méthodes proposées exploitent des données d’entrée statiques et dynamiques, elles se basent aussi sur différents types de descripteurs/représentations (géométrique, apparence et hybride). Après avoir évalué les performances des méthodes proposées avec des bases de données benchmark à savoir : JAFFE, KDEF, RaFD, CK+, MMI et MUG. L’objectif principal de ce travail de recherche réside dans l’utilisation des expressions faciales afin d’améliorer les performances des systèmes d’assistance existants. Ainsi, des expérimentations ont été conduites au sein de l’environnement intelligent LIARA afin de collecter des données de validation, et ce, en suivant un protocole d’expérimentation spécifique. Lors de l’exécution d’une tâche de la vie quotidienne (préparation du café), deux types de données ont été récoltés. Les données RFID ont permis de valider la méthode de reconnaissance automatique des actions utilisateurs ainsi que la détection automatique d’erreurs. Quant aux données faciales, elles ont permis d’évaluer la contribution des expressions faciales afin d’améliorer les performances du système d’assistance en termes de détection d’erreurs. Avec une réduction du taux de fausses détections dépassant les 20%, l’objectif fixé a été atteint avec succè