CONF Roy_BMVC2009_2009/IDIAP Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection Roy, Anindya Marcel, Sébastien EXTERNAL https://publications.idiap.ch/attachments/papers/2009/Roy_BMVC2009_2009.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Roy_Idiap-RR-28-2009 Related documents British Machine Vision Conference 2009 2009 September 2009 Face detection is the first step in many visual processing systems like face recognition, emotion recognition and lip reading. In this paper, we propose a novel feature called Haar Local Binary Pattern (HLBP) feature for fast and reliable face detection, particularly in adverse imaging conditions. This binary feature compares bin values of Local Binary Pattern histograms calculated over two adjacent image subregions. These subregions are similar to those in the Haar masks, hence the name of the feature. They capture the region-specific variations of local texture patterns and are boosted using AdaBoost in a framework similar to that proposed by Viola and Jones. Preliminary results obtained on several standard databases show that it competes well with other face detection systems, especially in adverse illumination conditions. REPORT Roy_Idiap-RR-28-2009/IDIAP Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection Roy, Anindya Marcel, Sébastien EXTERNAL https://publications.idiap.ch/attachments/reports/2009/Roy_Idiap-RR-28-2009.pdf PUBLIC Idiap-RR-28-2009 2009 Idiap September 2009 Face detection is the first step in many visual processing systems like face recognition, emotion recognition and lip reading. In this paper, we propose a novel feature called Haar Local Binary Pattern (HLBP) feature for fast and reliable face detection, particularly in adverse imaging conditions. This binary feature compares bin values of Local Binary Pattern histograms calculated over two adjacent image subregions. These subregions are similar to those in the Haar masks, hence the name of the feature. They capture the region-specific variations of local texture patterns and are boosted using AdaBoost in a framework similar to that proposed by Viola and Jones. Preliminary results obtained on several standard databases show that it competes well with other face detection systems, especially in adverse illumination conditions.