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Weakly Trained Parallel Classifier and CoLBP Features for Frontal Face Detection in Surveillance Applications

Face detection in video sequence is becoming popular in surveillance applications. The trade-off between obtaining discriminative features to achieve accurate detection versus computational overhead of extracting these features, which affects the classification speed, is a persistent problem. Two ideas are introduced to increase the features’ discriminative power. These ideas are used to implement two frontal face detectors examined on a 2D low-resolution surveillance sequence.
First contribution is the parallel classifier. High discriminative power features are achieved by fusing the decision from two different features trained classifiers where each type of the features targets different image structure. Accurate and fast to train classifier is achieved.
Co-occurrence of Local Binary Patterns (CoLBP) features is proposed, the pixels of the image are targeted. CoLBP features find the joint probability of multiple LBP features. These features have computationally efficient feature extraction and provide
high discriminative features; hence, accurate detection is achieved.

Identiferoai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/25775
Date10 January 2011
CreatorsLouis, Wael
ContributorsPlataniotis, Konstantinos N.
Source SetsUniversity of Toronto
Languageen_ca
Detected LanguageEnglish
TypeThesis

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