New algorithms generate improved templates for biometric recognition

Abstract

Face detection and eye gaze estimation are crucial components of applications such as virtual reality, video conferencing, video surveillance, face recognition and face database management.1–4 New applications involving human-machine interfaces are under development where eye gaze could be employed to control machines assisting handicapped individuals, or in technologically complex environments such as hospital operating rooms, airplane cockpits, or industrial control units.1, 5–7 Several limitations have been identified in existing face and iris detection methods: some are invasive; some focus on only one eye, do not follow head movements, or only follow very small head movements; some fail to detect eyes while blinking; and some require manual initialization. Additionally, some impose restrictions such as symmetry, which limits the background content. Other methods depend on face skin tone, which varies among different ethnic groups and is dependent upon nearby light sources. These limitations contribute to two particular problems that apply to most methods of face and eye detection: they cannot work in real time, and they are not robust for face rotations.1, 8–12 To allow real-time processing, our first approach was to use anthropometric templates for face and eye detection. This set of templates, which consists of different face sizes, includes key facial features such as eyebrows, nose, mouth, and the lower part of the chin, thus mainly limiting the search to these specific face and eye characteristics. For eye detection, the templates consider an eye region within an elliptical face contour. Our second approach was to design templates using PSO (particle swarm optimization) to search for the best templates,8 or use a local component maximization (CM) procedure.9 The Figure 1. (a) Coarse face detection: original image, coarse directional image, accumulator, superposition of accumulator over original image, and coarse face detection. (b) Fine face detection: original image, coarse face detection, fine directional image, anthropometric face templates, and face detection by face template.