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Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Audio-Visual Emotion Recognition in Video Clips |
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Journal Article |
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2019 |
Publication |
IEEE Transactions on Affective Computing |
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TAC |
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10 |
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1 |
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60-75 |
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This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases. |
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1 Jan.-March 2019 |
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HUPBA; 602.143; 602.133 |
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no |
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Admin @ si @ NMN2017 |
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3011 |
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Author |
Fatemeh Noroozi; Ciprian Corneanu; Dorota Kamińska; Tomasz Sapiński; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Survey on Emotional Body Gesture Recognition |
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Journal Article |
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2021 |
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IEEE Transactions on Affective Computing |
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TAC |
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12 |
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2 |
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505 - 523 |
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Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as “body language” and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations. |
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HUPBA; no proj |
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no |
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Admin @ si @ NCK2021 |
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3657 |
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Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya |
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Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” |
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2018 |
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Informaciones Psiquiatricas |
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232 |
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47-59 |
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0210-7279 |
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HUPBA; no menciona |
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no |
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Admin @ si @ FAA2018 |
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3214 |
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Eloi Puertas; Sergio Escalera; Oriol Pujol |
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Title |
Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification |
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2015 |
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Pattern Analysis and Applications |
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PAA |
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18 |
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2 |
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247-261 |
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Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification |
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In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. |
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Springer-Verlag |
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1433-7541 |
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HuPBA;MILAB |
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no |
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Admin @ si @ PEP2013 |
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2251 |
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Egils Avots; Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Baro; Paul Pallin; Gholamreza Anbarjafari |
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From 2D to 3D geodesic-based garment matching |
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2019 |
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Multimedia Tools and Applications |
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MTAP |
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78 |
Issue |
18 |
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25829–25853 |
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Shape matching; Geodesic distance; Texture mapping; RGBD image processing; Gaussian mixture model |
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A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset. |
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HuPBA; ISE; 600.098; 600.119; 602.133 |
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Admin @ si @ AME2019 |
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3317 |
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