Detection of Partially Occluded Face Using Support Vector Machines

Partially occluded face detection is need, because although the technology of the Automated Teller Machines and face detection is increased, we cannot control the people who wear sunglasses or mask for the crime. To reject the occluded face, we first trained the features of the normal faces and the occluded faces that wear sunglasses or mask using Principal Component Analysis and Support Vector Machines to reduce the dimension and classify eficiently. Then we decide that the detected face is normal or partially occluded face using the scheme that integrates the Principal Component Analysis and Support Vector Machines. In the experiments, we trained the 3200 normal face images that have the variations of illumination and expression and each 2900 and 4500 partially occluded face images that wear the sunglasses or mask with 60*25, and 60*35 resolution. We get the 95.2% and 98.8% partially occluded face detection ratio after face detection and 2.5% and 0% false alarm ratio from the experiments based on the Purdue University Face DB. The proposed algorithm which is incorporated the face detection system can help the security using the Digital Video Recorder System and face recognition