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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yan Yan Subramanian, R. Ricci, E. Lanz, O. Sebe, N. |
| Copyright Year | 2014 |
| Description | Author affiliation: Univ. of Trento, Trento, Italy (Yan Yan; Sebe, N.) || Adv. Digital Sci. Center (ADSC), Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Subramanian, R.) || Fondazione Bruno Kessler, Trento, Italy (Lanz, O.) || Dept. of Electr. & Inf. Eng., Univ. of Perugia, Perugia, Italy (Ricci, E.) |
| Abstract | Social attention behavior offers vital cues towards inferring one's personality traits from interactive settings such as round-table meetings and cocktail parties. Head orientation is typically employed as a proxy for determining the social attention direction when faces are captured at low-resolution. Recently, multi-task learning has been proposed to robustly compute head pose under perspective and scale-based facial appearance variations when multiple, distant and large field-of-view cameras are employed for visual analysis in smart-room applications. In this paper, we evaluate the effectiveness of an SVM-based MTL (SVM+MTL) framework with various facial descriptors (KL, HOG, LBP, etc.). The KL+HOG feature combination is found to produce the best classification performance, with SVM+MTL outperforming classical SVM irrespective of the feature used. |
| Starting Page | 4182 |
| Ending Page | 4187 |
| File Size | 661414 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.717 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face Training Cameras Support vector machines Accuracy Skin |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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