Abstract:

Aiming at the problem of low detection accuracy and high computational complexity of deep learning models due to factors such as imaging angle, weather conditions, and occlusion in face detection in video, a face detection algorithm based on ellipse skin color model and AdaBoost was proposed. The algorithm selectd Haar-like features as weak classifiers, and used the face images in the cropped CAS_PEAL data set as the training set, and used the AdaBoost algorithm to combine multiple weak classifiers into a strong classifier. The cascaded structure constituted the final classifier model. In order to solve the problem of detecting non-face area as a human face, an ellipse skin color model was introduced, and the video frame was processed using the ellipse skin color model so that areas with similar skin color in the image enterd the subsequent face detection process to reduce the false detection rate. Experimental results showed that the algorithm could perform real-time face detection at an average detection speed of 26 ms (single face video) and an average of 34 ms (multi-face video), and achieved a detection accuracy of 87.2%, which had a large application promotion value.

Key words: AdaBoost algorithm, Haar-like feature, integration diagram, ellipse skin color model, weak classifier, cascade, cascade classifier, skin color segmentation