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The facial analysis is a very active research field because it concerns numerous fields of application such as: biometrics, robotics, video games and telecommunications. Many high-level vision applications such as facial feature tracking, facial modeling and animation, facial expression analysis, and face recognition, require reliable feature extraction. In this paper, we propose a novel approach for invariant facial feature detection (eyes, nose and mouth) followed by a matching and for facial expression recognition with respect to the identity. To represent all regions of a given face, we proceed by the extraction of landmarks, which amounts to the extraction of a set of contours. [ABSTRACT FROM PUBLISHER]

An effective human computer interaction system should be equipped with mechanisms to recognize and respond to the affective state of the user. However, spoken message conveys different communicative aspects such as the verbal content, emotional state and idiosyncrasy of the speaker. Each of these aspects introduces variability that will affect the performance of an emotion recognition system. If the models used to capture the expressive behaviors are constrained by the lexical content and speaker identity, it is expected that the observed uncertainty in the channel will decrease, improving the accuracy of the system. Motivated by these observations, this study aims to quantify and localize the speaker, lexical and emotional variabilities observed in the face during human interaction. A metric inspired in mutual information theory is proposed to quantify the dependency of facial features on these factors. This metric uses the trace of the covariance matrix of facial motion trajectories to measure the uncertainty. The experimental results confirm the strong influence of the lexical information in the lower part of the face. For this facial region, the results demonstrate the benefit of constraining the emotional model on the lexical content. The ultimate goal of this research is to utilize this information to constrain the emotional models on the underlying lexical units to improve the accuracy of emotion recognition systems. [ABSTRACT FROM PUBLISHER]

One of the first steps in most facial expression and facial analysis systems is the localization of prominent facial feature points. In this paper we present a novel approach for facial feature point detection using Simplified Gabor Wavelets (SGW). The classifier is built in cascades, where each stage of the cascade is a Gentle-AdaBoost trained classifier. In addition, we suggest a confidence based weighted grouping of multi-detected feature points to enhance accuracy. We have trained and tested our algorithm with a shuffled mix of four available labeled databases with more than 700 individuals. Our experimental results achieve approximately 82% detection rate in average, which is a considerable result, since the databases contain not only frontal faces. [ABSTRACT FROM PUBLISHER]

This paper presents a novel dynamic curvature based approach (dynamic shape-index based approach) for 3D face analysis. This method is inspired by the idea of 2D dynamic texture and 3D surface descriptors. The dynamic texture (DT) based approaches [30][31][32] encode and model the local texture features in the temporal axis, and have achieved great success in applications of 2D facial expression recognition. In this paper, we propose a so-called Dynamic Curvature (DC) approach for 3D facial activity analysis. To do so, the 3D dynamic surface is described by its surface curvature-based shape-index information. The surface features are characterized in local regions along the temporal axis. A dynamic curvature descriptor is constructed from local regions as well as temporal domains. To locate the local regions, we also applied a 3D tracking model based method for detecting and tracking 3D features across 3D dynamic sequences. Our method is validated through our experiment on 3D facial activity analysis for distinguishing neutral vs. non-neutral expressions, prototypic expressions, and their intensities. [ABSTRACT FROM PUBLISHER]