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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/25775

Title: Weakly Trained Parallel Classifier and CoLBP Features for Frontal Face Detection in Surveillance Applications
Authors: Louis, Wael
Advisor: Plataniotis, Konstantinos N.
Department: Electrical and Computer Engineering
Keywords: Face detection
Feature extraction
Local Binary Patterns features
Weakly trained classifiers
Issue Date: 10-Jan-2011
Abstract: 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.
URI: http://hdl.handle.net/1807/25775
Appears in Collections:Master
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Master theses

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