Face detection based on multi-scale enhanced local texture feature sets

This paper presents a distinctive rectangle feature Multi-Scale Local Ternary Patterns (MS-LTP) for face detection. The MS-LTP is a generalization of the Local Ternary Patterns (LTP) [1] and is able to capture larger scale structures of faces. It's less sensitive to noise and more discriminative that can reduce the number of weak classifiers for the AdaBoost learning algorithm to construct a strong face/non-face classifier. The size of the MS-LTP feature set is also medium for the AdaBoost learning algorithm to select a proper set of features. Our experimental results on the CMU-MIT frontal face test set show that the MS-LTP outperforms Haar, Local Binary Patterns (LBP) under noisy conditions and the MS-LTP based face detector works more rapidly.

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