Multispectral face detection method based on graphics processing unit (GPU)

Abstract

The invention discloses a multispectral face detection method based on a graphics processing unit (GPU). The GPU based on compute unified device architecture (CUDA) is used for calculating an infrared light video and a visible light video which are recorded synchronously, so that features of a face in an infrared light image and a visible light image are detected respectively; and an infrared light detection result and a visible light detection result are combined synchronously, and a combined result is used as a face feature of a human and output. According to the multispectral face detection method, the face detection results based on the infrared light image and the visible light image are combined. The detection method is not influenced by light, and a detected face image is an accurate visible light image; and a face in an image can be detected under a severe environment. During detection, a support vector machine (SVM) classifier for classifying attitudes of faces is constructed; and due to the classification of the attitude classifier, face detection based on an adaboos detection algorithm is performed on sub types of infrared images. According to the technology, faces with various attitudes in the infrared images can be detected; and the limitation that only front faces in the infrared images can be detected is broken through.

Description

technical field [0001] The present invention relates to the field of person target query and search technology methods, in particular to a GPU-based multi-spectral face detection method. Background technique [0002] At present, the unilateral face detection methods for visible light images and infrared images can be divided into four categories, as follows, 1) methods based on prior knowledge. These prior-knowledge-based methods encode knowledge of what constitutes a typical human face. Usually, the prior knowledge contains the interrelationships among these facial features. Such methods are mainly used for face localization. A difficulty of this approach is how to convert face knowledge into well-defined guidelines. If the criteria are too detailed, some faces will be missed because they do not pass all the criteria. If the guidelines are too rough, many positive mistakes will be made. In addition, this method is difficult to extend to detect faces in different poses,...

AI Technical Summary

Problems solved by technology

But to further improve the detection accuracy, it is necessary to cascade more strong classifiers, but this will reduce the detection speed

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