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

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Face identification at a distance is very challenging since captured images are often degraded by blur and noise. Furthermore, the computational resources and memory are often limited in the mobile environments. Thus, it is very challenging to develop a real-time face identification system on the mobile device. This paper discusses face identification based on frequency domain matched filtering in the mobile environments. Face identification is performed by the linear or phase-only matched filter and sequential verification stages. The candidate window regions are decided by the major peaks of the linear or phase-only matched filtering outputs. The sequential stages comprise a skin-color test and an edge mask filtering test, which verify color and shape information of the candidate regions in order to remove false alarms. All algorithms are built on the mobile device using Android platform. The preliminary results show that face identification of East Asian people can be performed successfully in the mobile environments.