By Topic

Evolutionary pursuit and its application to face recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
C. Liu ; Dept. of Math. & Comput. Sci., Missouri Univ., St. Louis, MO, USA ; H. Wechsler

Introduces evolutionary pursuit (EP) as an adaptive representation method for image encoding and classification. In analogy to projection pursuit, EP seeks to learn an optimal basis for the dual purpose of data compression and pattern classification. It should increase the generalization ability of the learning machine as a result of seeking the trade-off between minimizing the empirical risk encountered during training and narrowing the confidence interval for reducing the guaranteed risk during testing. It therefore implements strategies characteristic of GA for searching the space of possible solutions to determine the optimal basis. It projects the original data into a lower dimensional whitened principal component analysis (PCA) space. Directed random rotations of the basis vectors in this space are searched by GA where evolution is driven by a fitness function defined by performance accuracy (empirical risk) and class separation (confidence interval). Accuracy indicates the extent to which learning has been successful, while separation gives an indication of expected fitness. The method has been tested on face recognition using a greedy search algorithm. To assess both accuracy and generalization capability, the data includes for each subject images acquired at different times or under different illumination conditions. EP has better recognition performance than PCA (eigenfaces) and better generalization abilities than the Fisher linear discriminant (Fisherfaces)

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:22 ,  Issue: 6 )