Face recognition tracker based on incremental learning algorithm

Abstract

The invention discloses a face recognition tracker based on Haar-like features and an incremental learning algorithm, and mainly relates to the field of computer vision and image processing. Accordingto the method, Haar-like feature evaluation is accelerated by using an integral graph, strong classifiers for distinguishing human faces and non-human faces are trained by using an AdaBoost algorithm, and the strong classifiers are cascaded together by using screening type cascading, so that the accuracy is improved. And the face tracking part predicts the position of the central point of the current time frame according to the position of the central point of the previous frame of image tracking frame. And main features of the image in the frame are extracted by using a PCA algorithm, and acorresponding dimension-reduced graph is predicted according to the position of the center point of the frame at the moment. A forgetting factor is introduced, and image data is updated once every five frames. The incremental algorithm does not need to train a model, so that the efficiency is improved. Theoretics and practices show that the method can automatically recognize a human face, when thedirection of the human face changes greatly, for example, when the front face becomes a side face, recognition and tracking can be continued, continuous recognition is kept, and interruption is avoided.

Description

technical field [0001] The invention belongs to a face recognition tracker based on Haar-like feature and incremental learning algorithm. Background technique [0002] With the reduction of computer costs and the development of computer vision technology, the field of computer vision has shown more and more applications. Among them, face recognition and tracking is a key application and plays an important role in many fields. [0003] The research on face recognition technology has become a hot research field and has been widely used. For example, in the field of public security, this technology is applied to video surveillance, customs identity verification, public security control, etc.; in the financial field, this technology is applied to identity verification of bank transactions, Internet payments, and bank card processing; The technology also has some interesting applications, such as smart housekeeping robots, virtual games with face recognition, etc. According to...

AI Technical Summary

Problems solved by technology

With the increase of tracking time, the calculation amount of each update of the feature basis vector will increase linearly, so using this method cannot achieve long-term face tracking, and consumes more computing power

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