Image analysis – Learning systems – Trainable classifiers or pattern recognizers
Reexamination Certificate
2004-06-01
2008-09-30
Mehta, Bhavesh M. (Department: 2624)
Image analysis
Learning systems
Trainable classifiers or pattern recognizers
C382S279000, C382S264000
Reexamination Certificate
active
07430315
ABSTRACT:
The face detection system and method attempts classification of a test image before performing all of the kernel evaluations. Many subimages are not faces and should be relatively easy to identify as such. Thus, the SVM classifier try to discard non-face images using as few kernel evaluations as possible using a cascade SVM classification. In the first stage, a score is computed for the first two support vectors, and the score is compared to a threshold. If the score is below the threshold value, the subimage is classified as not a face. If the score is above the threshold value, the cascade SVM classification function continues to apply more complicated decision rules, each time doubling the number of kernel evaluations, classifying the image as a non-face (and thus terminating the process) as soon as the test image fails to satisfy one of the decision rules. Finally, if the subimage has satisfied all intermediary decision rules, and has now reached the point at which all support vectors must be considered, the original decision function is applied. Satisfying this final rule, and all intermediary rules, is the only way for a test image to garner a positive (face) classification.
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Lim Jongwoo
Ohashi Takahiro
Ross David
Yang Ming-Hsuan
Duell Mark
Fenwick & West LLP
Honda Motor Co.
Mehta Bhavesh M.
Schaffer Jonathan
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