Face Detection Based On Near-Infrared Images In Embedded System

The task of face detection is to indicate whether there exist faces on specified image. If there are faces, the detection system should return the coordinate and range of faces. Face detection is an important part of face recognition technology. As the time goes by, it has been developed to an important course. The difficulties of face detection are light condition, age changing, accessory changing, attitude changing and etc. For embedded system, the cost of time and memory determine the availability of detection algorithm.In order to reduce the affect cause by light condition, lots of researches have been done in both industry and academic. Compare with improvement of algorithm, the more effective way is to use active Near-Infrared light system to perform face detection and recognition.This paper compares many face detection algorithms base on experience. Implements a Boosting trainer includes many algorithms, provides training SDK. To reduce the light condition affect, active Near-Infrared light system and camera are used. After the work of face area location, eye detection is also researched and compares the algorithm with other algorithm. To make the algorithm work on embedded system, this paper proposed lots of improved methods. It shows that it can improve the cost of time.This paper also formulates a face detection algorithm base on morphology. Face detection base on Haar feature and AdaBoost algorithm is used for locating the face area. And then normalize the area into specify size. Using the property of high reflection rate under Near-Infrared light on pupil, Quoit filter base on morphology is used for eye detection. In order to dealing with difference size of pupil, a multi-scale filter is proposed for reducing both of false positive rate and false negative rate.The experience shows that AdaBoost+LDA training method can lower the error rate more quickly than others. On the other hand, uses image processing on detection system can improve the detection rate. Multi-scale Quoit filter eye detection algorithm is more precise than AdaBoost eye detection. To conclude, the method proposed by this article is accuracy and fast. And it fits the requirement of real time face detection.