Facial emotion recognition using min-max similarity classifier

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods

Real-Time Emotion Recognition System using Facial Expressions and Soft Computing methodologies

Facial Expression conveys non-verbal cues, which plays an important role in interpersonal relations. The Cognitive Emotion AI system is the process of identifying the emotional state of a person. The main aim of our study is to develop a robust system which can detect as well as recognize human emotion from live feed. There are some emotions which are universal to all human beings like angry, sad, happy, surprise, fear, disgust and neutral. The methodology of this system is based on two stages- facial detection is done by extraction of Haar Cascade features of a face using Viola Jones algorithm and then the emotion is verified and recognized using Artificial Intelligence Techniques. The system will take image or frame as an input and by providing the image to the model the model will perform the preprocessing and feature selection after that it will be predict the emotional state

Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

We present techniques for improving performance driven facial animation, emotion recognition, and facial key-point or landmark prediction using learned identity invariant representations. Established approaches to these problems can work well if sufficient examples and labels for a particular identity are available and factors of variation are highly controlled. However, labeled examples of facial expressions, emotions and key-points for new individuals are difficult and costly to obtain. In this paper we improve the ability of techniques to generalize to new and unseen individuals by explicitly modeling previously seen variations related to identity and expression. We use a weakly-supervised approach in which identity labels are used to learn the different factors of variation linked to identity separately from factors related to expression. We show how probabilistic modeling of these sources of variation allows one to learn identity-invariant representations for expressions which can then be used to identity-normalize various procedures for facial expression analysis and animation control. We also show how to extend the widely used techniques of active appearance models and constrained local models through replacing the underlying point distribution models which are typically constructed using principal component analysis with identity-expression factorized representations. We present a wide variety of experiments in which we consistently improve performance on emotion recognition, markerless performance-driven facial animation and facial key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS

A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people’s facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism

Emotion Classification based on Expressions and Body Language using Convolutional Neural Networks

Recognizing human emotions based on a person\u27s body language is a complex neurological process that humans often take for granted. The visual pathway that propagates from the eye to the occipital lobe, breaking down images from basic features and building upon the previous layer, can be replicated in a computer using artificially intelligent algorithms in the field of computer vision using convolutional neural networks (CNN). The neural network, just like in the brain, models the interaction between neurons, with each neuron being represented by a mathematical function called a perceptron. Real-world images of humans in varying environments displaying emotion through variations in expression, body language, colorings, and features are manually labeled and used to train the CNN. This allows the algorithm to pick out features that occur in pictures of each emotion that will intelligently aid in relating body language, expressions and poses to an emotion, even if this relation is not defined previously. The accuracy of the classification is then analyzed by reviewing what features were extracted from the images and the network is retrained accordingly to output even more accurate results

Data mining in the analysis of crop signatures

El propósito de esta tesis es realizar dos aportaciones significativas en el campo de la agricultura de precisión. Ambas aportaciones persiguen el mismo objetivo, aumentar la eficacia y reducir los costes de los diagnósticos agronómicos integrales. En caso de conseguirlo aumentaría el número de explotaciones agrícolas que pueden apostar por la agricultura de precisión. Esa resultaría ser la opción más rentable tanto para el presente como para el futuro. Una explicación simplificada del proceso de diagnóstico agronómico sería adquisición de información relevante del cultivo e interpretación de la misma, los dos procesos a los que esta tesis dirige la atención. Se pretende mejorar la efectividad y capacidad de generalización de los modelos que estiman el estado nutricional de la planta a partir de medidas espectrales. También se trata de incorporar las técnicas geomáticas desarrolladas en las últimas décadas (GPS, GIS,…) a las metodologías clásicas para la interpretación de los niveles de los nutrientes en la planta. El objetivo es desarrollar una metodología para el diagnóstico agronómico de los campos, sería un proceso lógico deductivo que trabaja con evidencias obtenidas en el mismo campo. No serían precisos estudios previos y por tanto estaría a disposición de cualquier agricultor independientemente del área geográfica o cultivo.The purpose of this thesis is to make two significant contributions in the field of precision agriculture. Both contributions have the same objective, to increase efficiency and reduce the costs of comprehensive agronomic diagnosis. In case of achieving the objectives, this would increase the number of farms that can go for precision agriculture. It would be the most profitable option for the present and the future. A simplified explanation of the process for the agronomic diagnostic would be the acquisition of relevant information of the crop and interpretation of the same, the two processes to which this thesis directs the attention. It is intended to improve the effectiveness and generalizability of models that estimate the nutritional status of the plant from spectral measurements. On the other hand tries to incorporate the techniques developed in the last decades (GPS, GIS ...) to the classical methods for the interpretation of the levels of nutrients in the plant. The purpose is to develop a methodology to make agronomic diagnosis; it would be a deductive process that works with evidence obtained in the same field. No previous studies would be needed and therefore it would be available to all farmers, regardless of geographic area or crop

Weakly-Labeled Data and Identity-Normalization for Facial Image Analysis

RÉSUMÉ Cette thèse traite de l’amélioration de la reconnaissance faciale et de l’analyse de l’expression du visage en utilisant des sources d’informations faibles. Les données étiquetées sont souvent rares, mais les données non étiquetées contiennent souvent des informations utiles pour l’apprentissage d’un modèle. Cette thèse décrit deux exemples d’utilisation de cette idée. Le premier est une nouvelle méthode pour la reconnaissance faciale basée sur l’exploitation de données étiquetées faiblement ou bruyamment. Les données non étiquetées peuvent être acquises d’une manière qui offre des caractéristiques supplémentaires. Ces caractéristiques, tout en n’étant pas disponibles pour les données étiquetées, peuvent encore être utiles avec un peu de prévoyance. Cette thèse traite de la combinaison d’un ensemble de données étiquetées pour la reconnaissance faciale avec des images des visages extraits de vidéos sur YouTube et des images des visages obtenues à partir d’un moteur de recherche. Le moteur de recherche web et le moteur de recherche vidéo peuvent être considérés comme de classificateurs très faibles alternatifs qui fournissent des étiquettes faibles. En utilisant les résultats de ces deux types de requêtes de recherche comme des formes d’étiquettes faibles différents, une méthode robuste pour la classification peut être développée. Cette méthode est basée sur des modèles graphiques, mais aussi incorporant une marge probabiliste. Plus précisément, en utilisant un modèle inspiré par la variational relevance vector machine (RVM), une alternative probabiliste à la support vector machine (SVM) est développée. Contrairement aux formulations précédentes de la RVM, le choix d’une probabilité a priori exponentielle est introduit pour produire une approximation de la pénalité L1. Les résultats expérimentaux où les étiquettes bruyantes sont simulées, et les deux expériences distinctes où les étiquettes bruyantes de l’image et les résultats de recherche vidéo en utilisant des noms comme les requêtes indiquent que l’information faible dans les étiquettes peut être exploitée avec succès. Puisque le modèle dépend fortement des méthodes noyau de régression clairsemées, ces méthodes sont examinées et discutées en détail. Plusieurs algorithmes différents utilisant les distributions a priori pour encourager les modèles clairsemés sont décrits en détail. Des expériences sont montrées qui illustrent le comportement de chacune de ces distributions. Utilisés en conjonction avec la régression logistique, les effets de chaque distribution sur l’ajustement du modèle et la complexité du modèle sont montrés. Les extensions aux autres méthodes d’apprentissage machine sont directes, car l’approche est ancrée dans la probabilité bayésienne. Une expérience dans la prédiction structurée utilisant un conditional random field pour une tâche d’imagerie médicale est montrée pour illustrer comment ces distributions a priori peuvent être incorporées facilement à d’autres tâches et peuvent donner de meilleurs résultats. Les données étiquetées peuvent également contenir des sources faibles d’informations qui ne peuvent pas nécessairement être utilisées pour un effet maximum. Par exemple les ensembles de données d’images des visages pour les tâches tels que, l’animation faciale contrôlée par les performances des comédiens, la reconnaissance des émotions, et la prédiction des points clés ou les repères du visage contiennent souvent des étiquettes alternatives par rapport à la tâche d’internet principale. Dans les données de reconnaissance des émotions, par exemple, des étiquettes de l’émotion sont souvent rares. C’est peut-être parce que ces images sont extraites d’une vidéo, dans laquelle seul un petit segment représente l’étiquette de l’émotion. En conséquence, de nombreuses images de l’objet sont dans le même contexte en utilisant le même appareil photo ne sont pas utilisés. Toutefois, ces données peuvent être utilisées pour améliorer la capacité des techniques d’apprentissage de généraliser pour des personnes nouvelles et pas encore vues en modélisant explicitement les variations vues précédemment liées à l’identité et à l’expression. Une fois l’identité et de la variation de l’expression sont séparées, les approches supervisées simples peuvent mieux généraliser aux identités de nouveau. Plus précisément, dans cette thèse, la modélisation probabiliste de ces sources de variation est utilisée pour identité normaliser et des diverses représentations d’images faciales. Une variété d’expériences sont décrites dans laquelle la performance est constamment améliorée, incluant la reconnaissance des émotions, les animations faciales contrôlées par des visages des comédiens sans marqueurs et le suivi des points clés sur des visages. Dans de nombreux cas dans des images faciales, des sources d’information supplémentaire peuvent être disponibles qui peuvent être utilisées pour améliorer les tâches d’intérêt. Cela comprend des étiquettes faibles qui sont prévues pendant la collecte des données, telles que la requête de recherche utilisée pour acquérir des données, ainsi que des informations d’identité dans le cas de plusieurs bases de données d’images expérimentales. Cette thèse soutient en principal que cette information doit être utilisée et décrit les méthodes pour le faire en utilisant les outils de la probabilité.----------ABSTRACT This thesis deals with improving facial recognition and facial expression analysis using weak sources of information. Labeled data is often scarce, but unlabeled data often contains information which is helpful to learning a model. This thesis describes two examples of using this insight. The first is a novel method for face-recognition based on leveraging weak or noisily labeled data. Unlabeled data can be acquired in a way which provides additional features. These features, while not being available for the labeled data, may still be useful with some foresight. This thesis discusses combining a labeled facial recognition dataset with face images extracted from videos on YouTube and face images returned from using a search engine. The web search engine and the video search engine can be viewed as very weak alternative classifier which provide “weak labels.” Using the results from these two different types of search queries as forms of weak labels, a robust method for classification can be developed. This method is based on graphical models, but also encorporates a probabilistic margin. More specifically, using a model inspired by the variational relevance vector machine (RVM), a probabilistic alternative to transductive support vector machines (TSVM) is further developed. In contrast to previous formulations of RVMs, the choice of an Exponential hyperprior is introduced to produce an approximation to the L1 penalty. Experimental results where noisy labels are simulated and separate experiments where noisy labels from image and video search results using names as queries both indicate that weak label information can be successfully leveraged. Since the model depends heavily on sparse kernel regression methods, these methods are reviewed and discussed in detail. Several different sparse priors algorithms are described in detail. Experiments are shown which illustrate the behavior of each of these sparse priors. Used in conjunction with logistic regression, each sparsity inducing prior is shown to have varying effects in terms of sparsity and model fit. Extending this to other machine learning methods is straight forward since it is grounded firmly in Bayesian probability. An experiment in structured prediction using Conditional Random Fields on a medical image task is shown to illustrate how sparse priors can easily be incorporated in other tasks, and can yield improved results. Labeled data may also contain weak sources of information that may not necessarily be used to maximum effect. For example, facial image datasets for the tasks of performance driven facial animation, emotion recognition, and facial key-point or landmark prediction often contain alternative labels from the task at hand. In emotion recognition data, for example, emotion labels are often scarce. This may be because these images are extracted from a video, in which only a small segment depicts the emotion label. As a result, many images of the subject in the same setting using the same camera are unused. However, this data can be used to improve the ability of learning techniques to generalize to new and unseen individuals by explicitly modeling previously seen variations related to identity and expression. Once identity and expression variation are separated, simpler supervised approaches can work quite well to generalize to unseen subjects. More specifically, in this thesis, probabilistic modeling of these sources of variation is used to “identity-normalize” various facial image representations. A variety of experiments are described in which performance on emotion recognition, markerless performance-driven facial animation and facial key-point tracking is consistently improved. This includes an algorithm which shows how this kind of normalization can be used for facial key-point localization. In many cases in facial images, sources of information may be available that can be used to improve tasks. This includes weak labels which are provided during data gathering, such as the search query used to acquire data, as well as identity information in the case of many experimental image databases. This thesis argues in main that this information should be used and describes methods for doing so using the tools of probability