Abstract
Face detection and recognition have wide applications in robot vision and intelligent surveillance. However, face identification at a distance is very challenging because long-distance images are often degraded by low resolution, blurring and noise. This paper introduces a person-specific face detection method that uses a nonlinear optimum composite filter and subsequent verification stages. The filter's optimum criterion minimizes the sum of the output energy generated by the input noise and the input image. The composite filter is trained with several training images under long-distance modelling. The candidate facial regions are provided by the filter's outputs of the input scene. False alarms are eliminated by subsequent testing stages, which comprise skin colour and edge mask filtering tests. In the experiments, images captured by a webcam and a CCTV camera are processed to show the effectiveness of the person-specific face detection system at a long distance.