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Face Detection
Heads: Henry Schneiderman and Takeo Kanade
Contact: Henry Schneiderman (hws@ux1.sp.cs.cmu.edu)

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

Associated center: VASC
Associated lab/group: Face Group

For more information, see this project's homepage.

In this project we are developing computer methods which automatically locate human faces in still photographs and video. Our goal is to develop algorithms that are accurate and computationally efficient. A secondary goal is to apply these techniques to detections of other objects such as cars, text, cells in biomedical images, and everyday objects in an office setting such as staplers, pens, and keyboards.

Our approach is to use statistical modeling to capture the variation in facial appearance. Currently, we use a set of models that each describe the statistical behavior of a group of wavelet coefficients.

Our face detector is widely considered to to the most accurate for frontal face detection and the only known method that works reliably for non-frontal faces images. See face detection demo for an on-line demonstration of this algorithm. In addition, we have developed the only known method for finding passenger cars in photographic images.

In September 2004, several Carnegie Mellon University researchers launched Pittsburgh Pattern Recognition, Inc. to commercialize this technology. The company's founders include Dr. Henry Schneiderman, Professor Robert Lowe (Tepper School of Business), and Dr. Michael Nechyba (Robotics Institute, Ph.D).

Steve Milborrow at the University of Cape Town used this face detector in his Master's project.

Name - Title <Email Address>
• [Home] Takeo Kanade - U.A. and Helen Whitaker University Prof. <tk@cs.cmu.edu>
• Henry Schneiderman - Adjunct Faculty (Adjunct) <hws@ux1.sp.cs.cmu.edu>

• Face Detection Databases - A collection of databases for training and testing face detectors.
• Object Recognition Using Statistical Modeling - Automobile and human face detection via statistical modeling.

• Feature-Centric Evaluation for Efficient Cascaded Object Detection
  H. Schneiderman
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, June, 2004. [Abstract]
  Download: pdf [469 KB] copyrighted

• Learning a Restricted Bayesian Network for Object Detection
  H. Schneiderman
  IEEE Conference on Computer Vision and Pattern Recognition, IEEE, June, 2004. [Abstract]
  Download: pdf [567 KB] copyrighted

• Learning Statistical Structure for Object Detection
  H. Schneiderman
  Computer Analysis of Images and Patterns (CAIP), 2003, Springer-Verlag, August, 2003. [Abstract]
  Download: pdf [320 KB] copyrighted

• Object Detection Using the Statistics of Parts
  H. Schneiderman and T. Kanade
  International Journal of Computer Vision, 2002. [Abstract]
  Download: pdf [3828 KB] copyrighted

• A Statistical Model for 3D Object Detection Applied to Faces and Cars
  H. Schneiderman and T. Kanade
  IEEE Conference on Computer Vision and Pattern Recognition, IEEE, June, 2000. [Abstract]
  Download: pdf [474 KB], ps.gz [1662 KB] copyrighted

• A Statistical Approach to 3D Object Detection Applied to Faces and Cars
  H. Schneiderman
  doctoral dissertation, tech. report CMU-RI-TR-00-06, Robotics Institute, Carnegie Mellon University, May, 2000. [Abstract]
  Download: pdf [1947 KB], ps.gz [5938 KB] copyrighted

• Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
  H. Schneiderman and T. Kanade
  Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '98), July, 1998, pp. 45-51. [Abstract]
  Download: pdf [255 KB], ps.gz [787 KB] copyrighted

• Rotation Invariant Neural Network-Based Face Detection
  H. Rowley, S. Baluja, and T. Kanade
  Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June, 1998. [Abstract]
  Download: pdf [1478 KB], ps.gz [6370 KB] copyrighted

• Neural Network-Based Face Detection
  H. Rowley, S. Baluja, and T. Kanade
  IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, January, 1998, pp. 23-38. [Abstract]
  Download: pdf [1411 KB], ps.gz [4621 KB] copyrighted

• Rotation Invariant Neural Network-Based Face Detection
  H. Rowley, S. Baluja, and T. Kanade
  tech. report CMU-CS-97-201, Computer Science Department, Carnegie Mellon University, December, 1997. [Abstract]
  Download: pdf [1574 KB], ps.gz [6372 KB] copyrighted