Face detection method with characteristic reduction

Abstract

The invention relates to a characteristic reduction-based face detection method in the technical field of pattern recognition. The face detection method comprises the following steps: step 1. reducing Haar-like characteristics, calculating tonal value of each Haar-like characteristic through an integrogram for positive and negative samples, then acquiring peak values of accumulative tonal value histograms for the positive and negative samples, judging recognition capability of the characteristic for a face or a non-face according to relative positions of the peak values of the accumulative tonal value histograms for the positive and negative samples, and determining whether the characteristics is removed or retained; step 2 training the reduced Haar-like characteristics and selecting an optimal weak classifier meeting conditions; and step 3 detecting a face picture by the optimal weak classifier obtained by training. The method ensures the accuracy of face detection, simultaneously solves the problem of time consumption lying in an AdaBoost method. In addition, the method is also applied in the fields of security access control, video monitoring, content-based retrieval, new generation of man-machine interface, and the like.

Description

technical field [0001] The present invention relates to a face detection method in the technical field of image processing, in particular to a face detection method with simplified features. Background technique [0002] Face detection is to detect the human face from the image background. Due to the influence of image background, brightness changes and human head posture and other factors, face detection has become a complex and challenging research topic. At present, the AdaBoost training processing method based on the cascade structure is considered to be the most effective face detection method. However, the AdaBoost method is a method based on sample learning, which requires a lot of time for training. Even for a medium-sized training sample set, to achieve better detection results, the training time is often as long as several days. This greatly limits the further development of the AdaBoost method in the application of target detection. [0003] One of the main reas...

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Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the prior art, to propose a face detection method with simplified features, while ensuring the accuracy of face detection, it better solves the time-consuming problem of AdaBoost training processing technology

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