Learning Flexible Block based Local Binary Patterns for unconstrained face detection

Face detection has been a very active research topic in recent years. However, when applied to uncontrolled environments, some systems exhibit poor generalization ability. Even though few of existing methods can achieve promising results in some challenging situations, they usually have the requirement of high computational cost. This will definitely limit the use of those methods in some mobile platforms which have limited computational resources and strict power-consumption control. In this paper, a novel facial representation method for multi-view face detection in uncontrolled environment is presented. The proposed method, named Flexible Block based Local Binary Patterns (FBLBP), has low storage requirements and it is fast to compute; while its performance is comparable with the state of the art methods, demonstrated on the challenging Face Detection Data set and Benchmark (FDDB).