A fast and robust face location and feature extraction system

Automatic face location and feature detection in front view images has a wide range of usage. To fulfill a both fast and robust algorithm is still a challenge. In this paper, we have proposed such a solution. In order to detect faces fast and accurate in various images, we have adopted a Gabor-like filtering scheme to locate all the possible features, followed by a verification procedure to check the "faceness" of all possible feature-blob combinations. Then the different features are segmented using integral projection. To detect the contours of different features precisely, we propose a heuristic knowledge-based contour tracking algorithm, using an adaptive oriented edge filtering and tracking scheme. We also have proposed a novel blob detector, which can detect blob-shaped dark image patterns (e.g. the iris, the nostril) efficiently. Our algorithm is fast, robust and accurate, which is proved by experiments on a pretty large database. Even in case of strange lighting, low-resolution, or strong distraction from other facial structures (such as facial hair), our algorithm can also work without serious deterioration in performance.