Face detection method and device based on multiple classifiers

Abstract

The invention provides a face detection method based on multiple classifiers, comprising the following steps: selecting positive sample images and negative sample images, and clustering the positive sample images through a K-Means clustering algorithm to get clustered positive sample sets; training the clustered positive sample sets and the negative sample images through a main classifier and sub classifiers by use of a Cascade algorithm to get a trained main classifier and trained sub classifiers; and inputting an image, detecting the input image according to the trained main classifier and the trained sub classifiers, and outputting a detection result. Compared with the prior art, a face can be detected from a complex image, and the detection accuracy is high.

Description

technical field [0001] The invention relates to image processing, video monitoring and security protection, in particular to a face detection method and device. Background technique [0002] Face detection is the process of detecting the position and size of all faces (if they exist) in a specified image. It was originally proposed as the positioning of an automatic face recognition system. The application value of content retrieval and new-generation human-computer interface has become a key technology in face information processing and has received extensive attention. [0003] Currently, face detection methods are mainly divided into two types: knowledge-based face detection and statistics-based face detection. Among them, knowledge-based face detection includes: template matching, face features, shape and edge, texture features, color features, etc.; statistics-based face detection includes: neural network, support vector machine (SVM), hidden Margin Cove model, Boost ...

AI Technical Summary

Problems solved by technology

However, the above face detection methods are less robust

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