A Study on Components and Features in Face Detection

It's natural and convenient that the face is used as a feature in the city safety monitoring. Components and feature extraction are two key problems in face detection. To address the local occlusion and pose variation in face detection, face can be looked on as a whole composed of several parts from up to down. First, the face is divided into a number of local regions from which various features are extracted. Each region is identified by a local classifier and is assigned a preliminary part label. A random field is established based on these labels and multiple dependencies between different parts are modeled in a CRF framework. The probability that the test image may be a face is calculated by a trained CRF model. The probability is used as a measure to test the existence of a face. The experiments were carried out on the CMU/MIT dataset. As indicated by the experiment results, the following methods can improve the detection rate and enhance the robustness of face detection in case of occlusion: 1) integrating multiple features and multiple dependencies in CRF framework; 2) dividing the face optimally.