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Ramin Irani, Kamal Nasrollahi, Chris Bahnsen, D.H. Lundtoft, Thomas B. Moeslund, Marc O. Simon, et al. (2015). Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 88–95).
Abstract: Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal
facial images for pain detection and pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames improving more than 6% over RGB only analysis in similar conditions.
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Andres Traumann, Sergio Escalera, & Gholamreza Anbarjafari. (2015). A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 75–79).
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Xavier Baro, Jordi Gonzalez, Junior Fabian, Miguel Angel Bautista, Marc Oliu, Hugo Jair Escalante, et al. (2015). ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 1–9).
Abstract: Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.
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