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David Masip, M. Bressan, & Jordi Vitria. (2005). Feature extraction methods for real-time face detection and classification. Eurasip Journal on Applied Signal Processing, 13: 2061–2071.
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Matthias S. Keil, & Jordi Vitria. (2007). Pushing it to the Limit: Adaptation with Dynamically Switching Gain Control. EURASIP Journal on Advances in Signal Processing, Vol 2007, Article ID 51684, 10 pages, doi: 10.1155/2007/51684.
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Sergio Escalera, Oriol Pujol, Petia Radeva, Jordi Vitria, & Maria Teresa Anguera. (2010). Automatic Detection of Dominance and Expected Interest. EURASIPJ - EURASIP Journal on Advances in Signal Processing, , 12.
Abstract: Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.
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Laura Igual, Agata Lapedriza, & Ricard Borras. (2013). Robust Gait-Based Gender Classification using Depth Cameras. EURASIPJ - EURASIP Journal on Advances in Signal Processing, 37(1), 72–80.
Abstract: This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section.
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Sergio Escalera, R. M. Martinez, Jordi Vitria, Petia Radeva, & Maria Teresa Anguera. (2010). Deteccion automatica de la dominancia en conversaciones diadicas. EP - Escritos de Psicologia, 3(2), 41–45.
Abstract: Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers' opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences.
Keywords: Dominance detection; Non-verbal communication; Visual features
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