/Human Sensing

Human Sensing

Portrait of Human Sensing
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The goal of the Human Sensing Lab is to develop new machine learning algorithms to model and understand human behavior from sensory data (e.g. video, motion capture, audio). Our work is motivated by applications in the fields of human health, biometrics and human-machine interface. For more information see www.humansensing.cs.cmu.edu

Displaying 17 Publications
Selective Transfer Machine for Personalized Facial Expression Analysis
Wen-Sheng Chu, Fernando De la Torre Frade and Jeffrey Cohn

Journal Article, IEEE Transactions on Pattern Analysis and Machine Intelligence, March, 2016
Confidence Preserving Machine for Facial Action Unit Detection
Jiabei Zeng, Wen-Sheng Chu, Fernando De la Torre Frade, Jeffrey Cohn and Zhang Xiong

Conference Paper, International Conference on Computer Vision (ICCV), December, 2015
Unsupervised Synchrony Discovery in Human Interaction
Wen-Sheng Chu, Jiabei Zeng, Fernando De la Torre Frade, Jeffrey Cohn and Daniel S. Messinger

Conference Paper, International Conference on Computer Vision (ICCV), December, 2015
Joint Patch and Multi-label Learning for Facial Action Unit Detection
Kaili Zhao, Wen-Sheng Chu, Fernando De la Torre Frade, Jeffrey Cohn and Honggang Zhang

Conference Paper, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2015
IntraFace
Fernando De la Torre Frade, Wen-Sheng Chu, Xuehan Xiong, Francisco Vicente Carrasco, Xiaoyu Ding and Jeffrey Cohn

Conference Paper, Automatic Face and Gesture Recognition, May, 2015
Facial Action Unit Detection by Cascade of Tasks
Xiaoyu Ding, Wen-Sheng Chu, Fernando De la Torre Frade, Jeffrey Cohn and Qiao Wang

Conference Paper, International Conference on Computer Vision (ICCV), December, 2013
Selective Transfer Machine for Personalized Facial Action Unit Detection
Wen-Sheng Chu, Fernando De la Torre Frade and Jeffrey Cohn

Conference Paper, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2013
Supervised Descent Method and its Applications to Face Alignment
Xuehan Xiong and Fernando De la Torre Frade

Conference Paper, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), May, 2013
Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion
Feng Zhou, Fernando De la Torre Frade and Jessica K. Hodgins

Journal Article, Carnegie Mellon University, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 35, No. 3, pp. 582-596, March, 2013
Unsupervised Temporal Commonality Discovery
Wen-Sheng Chu, Feng Zhou and Fernando De la Torre Frade

Conference Paper, European Conference on Computer Vision (ECCV), October, 2012
Source Constrained Clustering
Ekaterina Taralova, Fernando De la Torre Frade and Martial Hebert

Conference Paper, 13th International Conference on Computer Vision 2011, November, 2011
Latent Gaussian Mixture Regression for Human Pose Estimation
Yan Tian, Leonid Sigal, Hernan Badino, Fernando De la Torre Frade and Yong liu

Conference Paper, Asian Conference on Computer Vision (ACCV), November, 2010
Canonical Time Warping for Alignment of Human Behavior
Feng Zhou and Fernando De la Torre Frade

Conference Paper, Advances in Neural Information Processing Systems Conference (NIPS), December, 2009
Temporal Segmentation and Activity Classification from First-person Sensing
Ekaterina H. Spriggs, Fernando De la Torre Frade and Martial Hebert

Conference Paper, IEEE Workshop on Egocentric Vision, CVPR 2009, June, 2009
Aligned Cluster Analysis for Temporal Segmentation of Human Motion
Feng Zhou, Fernando De la Torre Frade and Jessica K. Hodgins

Conference Paper, IEEE Conference on Automatic Face and Gestures Recognition, September, 2008
Facial Feature Detection with Optimal Pixel Reduction SVMs
Minh Hoai Nguyen, Joan Perez and Fernando De la Torre Frade

Conference Paper, 8th IEEE International Conference on Automatic Face and Gesture Recognition, September, 2008
Learning Image Alignment without Local Minima for Face Detection and Tracking
Minh Hoai Nguyen and Fernando De la Torre Frade

Conference Paper, 8th IEEE International Conference on Automatic Face and Gesture Recognition, September, 2008
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