Feature selection for face detection (2000) [5 citations — 3 self]
by Thomas Serre, Bernd Heisele, Sayan Mukherjee, Tomaso Poggio
AI Memo 1697, Massachusetts Institute of Technology
ftp://publications.ai.mit.edu/ai-publications/1500-1999/AIM-1697.ps
Add To MetaCart
Abstract:
This publication can be retrieved by anonymous ftp to publications.ai.mit.edu. The pathname for this publication is: ai-publications/1500-1999/1697 We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision func-tion of the SVM.
Citations
| 4514 | Statistical Learning Theory – Vapnik - 1998 |
| 118 | Feature selection for SVMs – Weston, Mukherjee, et al. |
| 106 | Rotation invariant neural network-based face detection – Rowley, Baluja, et al. - 1998 |
| 51 | Face detection in still gray images – Heisele, Poggio, et al. - 2000 |
| 47 | Learning and Example Selection for Object and Pattern Recognition – Sung - 1995 |
