Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications. (19th December 2010)

• Record Type: Journal Article
• Authors: Louis Louis, Wael Wael; Plataniotis Plataniotis, K. N. K. N.
• Other Names: Di Stefano Di Stefano Luigi Luigi Academic Editor.
• Is Part Of: EURASIP journal on image and video processing. Volume 2011(2011)
• Publication Date: 2010-12-19
• Subjects: Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; Traitement d'images; Vidéo numérique; Digital video; Image processing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 621.367
• DOI: 10.1155/2011/745487
• Languages: English
• ISSNs: 1687-5176

Abstract

Abstract : Face detection in video sequence is becoming popular in surveillance applications. The tradeoff between obtaining discriminative features to achieve accurate detection versus computational overhead of extracting these features, which affects the classification speed, is a persistent problem. This paper proposes to use multiple instances of rotational Local Binary Patterns (LBP) of pixels as features instead of using the histogram bins of the LBP of pixels. The multiple features are selected using the sequential forward selection algorithm we called Co-occurrence of LBP (CoLBP). CoLBP feature extraction is computationally efficient and produces a high-performance rate. CoLBP features are used to implement a frontal face detector applied on a 2D low-resolution surveillance sequence. Experiments show that the CoLBP face features outperform state-of-the-art Haar-like features and various other LBP features extensions. Also, the CoLBP features can tolerate a wide range of illumination and blurring changes.