Face detection and tracking

Abstract

A face detection apparatus for tracking a detected face between images in a video sequence comprises: a first face detector for detecting the presence of face(s) in the images; a second face detector for detecting the presence of face(s) in the images; the first face detector having a higher detection threshold than the second face detector, so that the second face detector is more likely to detect a face in an region in which the first face detector has not detected a face; and a face position predictor for predicting a face position in a next image in a test order of the video sequence on the basis of a detected face position in one or more previous images in the test order of the video sequence; in which: if the first face detector detects a face within a predetermined threshold image distance of the predicted face position, the face position predictor uses the detected position to produce a next position prediction; if the first face detector fails to detect a face within a predetermined threshold image distance of the predicted face position, the face position predictor uses a face position detected by the second face detector to produce a next position prediction.

Description

technical field [0001] The present invention relates to face detection. Background technique [0002] Many face detection algorithms have been proposed in the literature, including the use of so-called eigenfaces, facial template matching, deformable template matching or neural network classification. None of them are perfect, and each generally has associated advantages and disadvantages. None provide an absolutely reliable indication that an image contains a face; instead, they are all based on probability assessments, on a mathematical analysis of the image and whether it has at least some probability of containing a face. Depending on their application, algorithms typically set the threshold likelihood rather high in an attempt to avoid false detections of faces. [0003] Face detection in video material comprising a sequence of captured images is somewhat more complex than detecting faces in still images. In particular, it is desirable that a face detected in one ima...

AI Technical Summary

Problems solved by technology

On the other hand, if the threshold likelihood is set low, the proportion of false detections will increase and objects that are not faces may be successfully tracked throughout the image sequence

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