Neural Network-Based Face Detection

摘要

Object detection is a fundamental problem in computer vision. For such applications as image indexing, simply knowing the presence or absence of an object is useful. Detection of faces, in particular, is a critical part of face recognition and, and critical for systems which interact with users visually. This thesis introduces some solutions to these subproblems for the face detection domain. A neural network first estimates the orientation of any potential face. The image is then rotated to an upright orientation and preprocessed to improve contrast, reducing its variability. Next, the image is fed to a frontal, half profile, or full profile face detection network. Supervised training of these networks requires examples of faces and nonfaces. Face examples are generated by automatically aligning labelled face images to one another. Nonfaces are collected by an active learning algorithm, which adds false detections into the training set as training progresses. Arbitration between multiple networks and heuristics, such as the fact that faces rarely overlap in images, improve the accuracy. Use of fast candidate face selection, skin color detection, and change detection allows the upright and tilted detectors to run fast enough for interactive demonstrations, at the cost of slightly lower detection rates.