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People Image Analysis Consortium (PIA)
Head: Takeo Kanade
Contact: Jun-Su Jang (jsjang@cs.cmu.edu)

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

Associated center: VASC

For more information, see this lab's homepage.

The People Image Analysis (PIA) Consortium develops and distributes technologies that process images and videos to detect, track, and understand peoples' faces, bodies, and activities. The areas of technology that the PIA Consortium focus on include detection and tracking of humans, face recognition, facial expression analysis, gait analysis, and activity recognition. The goal of the Consortium is to develop a comprehensive set of imaging and processing tools, systems, and subsystems that work in the real-world environment.

Please see our official homepage for further information.

Name - Title <Email Address>
• [Home] Ankur Datta - PhD Student, RI <adatta@andrew.cmu.edu>
• [Home] Lie Gu - PhD Student, CS <lgu@andrew.cmu.edu>
• Jun-Su Jang - Postdoctoral Fellow <jsjang@cs.cmu.edu>
• [Home] Takeo Kanade - U.A. and Helen Whitaker University Prof. <tk@cs.cmu.edu>
• [Home] Yan Ke - PhD Student, CS <yke@cmu.edu>
• Yan Li - PhD Student, ECE <yanli@cs.cmu.edu>
• Yaser Ajmal Sheikh - Postdoctoral Fellow <yaser@andrew.cmu.edu>

• 3D Head Motion Recovery in Real Time - A cylindrical model-based algorithm recovers the full motion (3D rotations and 3D translations) of the head in real time.
• AAM Fitting Algorithms - Many varieties of algorithms for fitting Cootes and Taylor's "Active Appearance Models" are developed.
• Accurate Camera Calibration from Planar Patterns - A novel camera calibration method can increases not only an accuracy of intrinsic camera parameters but also an accuracy of stereo camera calibration by utilizing a single framework for square, circle, and ring planar calibration patterns.
• Event Detection in Videos - Our event detection method can detect a wide range of actions in video by correlating spatio-temporal shapes to over-segmented videos without background subtraction.
• Feature-based 3D Head Tracking - A feature-based head tracking algorithm can handle occlusions and fast motion of face.
• Frontal Face Alignment - This face alignment method detects generic frontal faces with large appearance variations and 2D pose changes and identifies detailed facial structures in images.
• Hand Tracking and 3-D Pose Estimation - A 2-D and 3-D model-based tracking method can track a human hand rapidly moving and deformed on complicated backgrounds and recover its 3-D pose parameters.
• Multi-People Tracking - Our multi-people tracking method can automatically initialize and terminate paths of people and follow multiple and changeable number of people on cluttered scenes over long time intervals.
• Multi-view Car Detection and Registration - This method can detect cars with occlusions and varying viewpoints from a single still images by using multi-class boosting algorithm.
• Real-time Face Detection - A face detection system has an accurate detection rate and real time performance by using an ensemble of weak classifiers.
• Spatio-Temporal Facial Expression Segmentation - A two-step approach temporally segment facial gestures from video sequences. It can register the rigid and non-rigid motion of the face.

• Hand Posture Estimation in Complex Backgrounds by Considering
  A. Imai, N. Shimada, and Y. Shirai
  Proc. of Asian Conf. on Computer Vision (ACCV) 2007, November, 2007, pp. 596 - 607.

• Temporal Segmentation of Facial Behavior
  F. De la Torre Frade, J. Campoy, Z. Ambadar, and J.F. Cohn
  International Conference on Computer Vision, October, 2007. [Abstract]
  Download: pdf [3061 KB] copyrighted

• Simultaneous registration and clustering for temporal segmentation of facial gestures from video
  F. De la Torre Frade, J. Campoy, J. Cohn, and T. Kanade
  2nd International Conference on Computer Vision Theory and Applications, March, 2007. [Abstract]

• Object Recognition by Observing Grasping Scene
  H. Kasahara and N. Shimada
  Proceedings the 13th Japan-Korea Joint Workshop on Frontiers of Computer Vision (FCV07), January, 2007, pp. 375 - 378.

• Automatic Clustering of Faces in Meetings
  C. Vallespi-Gonzalez, F. De la Torre Frade, M. Veloso, and T. Kanade
  ICIP 2006, October, 2006, pp. 1841-1844. [Abstract]
  Download: pdf [455 KB], ps.gz [4076 KB] copyrighted

• Evaluating Error Functions for Robust Active Appearance Models
  B. Theobald, I. Matthews, and S. Baker
  Proceedings of the International Conference on Automatic Face and Gesture Recognition, April, 2006, pp. 149 - 154. [Abstract]
  Download: pdf [229 KB] copyrighted

• On the Dimensionality of Deformable Face Models
  I. Matthews, J. Xiao, and S. Baker
  tech. report CMU-RI-TR-06-12, Robotics Institute, Carnegie Mellon University, March, 2006. [Abstract]
  Download: pdf [1489 KB] copyrighted

• Parameterizing Homographies
  S. Baker, A. Datta, and T. Kanade
  tech. report CMU-RI-TR-06-11, Robotics Institute, Carnegie Mellon University, March, 2006. [Abstract]
  Download: pdf [255 KB] copyrighted

• Active Appearance Models with Occlusion
  R. Gross, I. Matthews, and S. Baker
  Image and Vision Computing, Vol. 24, No. 6, 2006, pp. 593-604. [Abstract]
  Download: pdf [3404 KB] copyrighted

• Generic vs. person specific active appearance models
  R. Gross, I. Matthews, and S. Baker
  Image and Vision Computing, Vol. 23, No. 11, November, 2005, pp. 1080-1093. [Abstract]
  Download: pdf [2086 KB] copyrighted