Frontal face detection for surveillance purposes using dual Local Binary Patterns features

Abstract

Face detection in video sequence is becoming popular in surveillance applications, but the usage of large number of features and the long training time are persistent problems. This paper integrates two types of Local Binary Patterns (LBP) features in order to achieve a high detection rate with a high discriminative power face detector. First LBP feature is a novel way of using the Circular LBP, in which the pixels of the image are targeted; it is a non-computationally expensive feature extraction. The second LBP feature is the LBP Histogram, in which regions in the image are targeted; it is more computationally expensive than Circular LBP features but has higher discriminative power. The proposed detector is examined on real-life low-resolution surveillance sequence. Conducted experiments show that the proposed detector achieves 98% detection rate in comparison to 91% for the Lienhart detector. The proposed detector tolerates wide range of illumination changes.