FACIAL FEATURE EXTRACTION USING LOCAL BINARY PATTERN AND LOCAL TERNARY PATTERN WITH GRADIENT BASED ILLUMINATION NORMALIZATION
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Abstract
This paper presents a novel method of facial feature extraction along with illumination normalization. To suppress the effect of illumination, a gradient based illumination normalization technique is used in the pre-processing stage. Facial features are extracted using two local feature extractors Local Binary Pattern and Local Ternary Pattern. Local Binary Pattern is a very efficient method of feature extraction which is insensitive to monotonic grayscale variations in the image. A face image is split into small blocks and LBP histograms are computed for each sub block and then concatenated into a single feature vector. Local Ternary Patterns is noise resistant 3-state version of LBP. The facial feature extraction process is performed on the two very popular face databases Extended Yale B and AR database. The feature vectors obtained from LBP and LTP are highly discriminative and useful for further recognition tasks
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