Evolutionary pursuit and its application to face recognition
Liu, C.
Wechsler, H.
Dept. of Math. & Comput. Sci., Missouri Univ., St. Louis, MO;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 2000
Volume: 22,
Issue: 6
On page(s): 570-582
ISSN: 0162-8828
References Cited: 43
CODEN: ITPIDJ
INSPEC Accession Number: 6693740
Digital Object Identifier: 10.1109/34.862196
Current Version Published: 2002-08-06
Abstract
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)
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