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adaboost FEATURE SELECTION USING ADABOOST FOR FACE EXPRESSION RECOGNITION-FEATURE SELECTION USING ADABOOST FOR FACE EXPRESSION RECOGNITION
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Very recently tracking was approached using classification techniques such
as support vector machines. The object to be tracked is discriminated by a
classifier from the background. In a similar spirit we propose a novel on-line
AdaBoost featur
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this codes is Source code for face detection of viola paper.of its Features is:
Feature Computation: The “Integral” image representation
Feature Selection: The AdaBoost training algorithm .
Real-timeliness: A cascade of classifiers.-this code
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This paper presents an online feature selection algorithm
for video object tracking. Using the object and background
pixels from the previous frame as training samples, we model the
feature selection problem as finding a good subset of features to
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