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David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson |
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Title |
Automated Prediction of Preferences Using Facial Expressions |
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2014 |
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PloS one |
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Plos |
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9 |
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2 |
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e87434 |
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We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. |
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OR;MV |
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no |
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Admin @ si @ MNT2014 |
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2453 |
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Author |
Laura Igual; Agata Lapedriza; Ricard Borras |
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Title |
Robust Gait-Based Gender Classification using Depth Cameras |
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Journal Article |
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2013 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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37 |
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1 |
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72-80 |
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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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MILAB; OR;MV |
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Admin @ si @ ILB2013 |
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2144 |
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Onur Ferhat; Fernando Vilariño |
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Title |
Low Cost Eye Tracking: The Current Panorama |
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2016 |
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Computational Intelligence and Neuroscience |
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Article ID 8680541 |
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Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools. |
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MV; 605.103; 600.047; 600.097;SIAI |
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Admin @ si @ FeV2016 |
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2744 |
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Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey |
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Automatic segmentation and inpainting of specular highlights for endoscopic imaging |
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2010 |
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EURASIP Journal on Image and Video Processing |
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EURASIP JIVP |
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2010 |
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9 |
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800 |
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MV |
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fernando @ fernando @ |
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2423 |
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Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera |
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Automatic Detection of Dominance and Expected Interest |
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2010 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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12 |
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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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1110-8657 |
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OR;MILAB;HUPBA;MV |
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BCNPCL @ bcnpcl @ EPR2010d |
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1283 |
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