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Adaboost for face detection code works well
4.0
In 2001, the Adaboost algorithm was first applied to the face detection applications, and got very good results, and has made great achievements in human face detection: 1) of facial images using Haar features to describe the distribution of gray, as Harr features easy availability, improves detection rate; 2) pick up some sample capabilities of Haar features combine to form the strong classifiers; 3) constructed from coarse to fine lines cascade face detector, first with the strong classifiers regardless of the background and pictures of the most regional filtering, and then enhance classifier complexity, continued to filter out the remaining independent regions, what is left is the face, the ways to achieve the goal of improved face detection speed. The resources implementation of Adaboost for face detection.
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LBP algorithm for face recognition
no vote
Local binary pattern LBP (Local Binary Patterns) is an algorithm derived from the local texture definition, so-called textures are commonly used for image analysis characteristics, which contained information to characterize surface changes. Since 1996, Ojala of which the LBP operator has been classified due to their excellent properties and ease of calculation, making it widely used in image retrieval, face detection, analysis and industrial areas. The realization of this resource is the LBP face recognition code.
wilsonlv89 0 0
1
Face recognition intelligent attendance system wri
4.0
Complete MFC to write intelligent attendance system based on face recognition, contains developer documentation. Based on the Windows operating system, written in VS2008 SP environment, part algorithm using OpenCV 2.1, ASM face alignment of these parts also uses the OpenCV 2.0; the demo interface using MFC. Demonstrates the functionality of the program is that the specified image to the camera or user, to detect human faces, and then in the face of the stored (registered by a camera or picture) to find the closest match in the face and display. Others face database and classification of import/export functions.
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1
Skin color segmentation of face detection
no vote
Face detection using skin color segmentation is simple, high detection rate characteristics and that face has rotated and tilted faces also have better performance. But when the test will be similar to the skin color of some other parts of the body and is similar to the color of background error detection for face, has a high false positive rate. Face detection based on skin color segmentation and needs high light conditions, such as external environment, the external environment will directly affect the test results. Face detection using Adaboost algorithm has good performance, but this method is very complicated and slow testing speed, rotate and tilt of the face image detection rate is not high, and when the background is very complex algorithm of the false positive rate is high. Therefore, this code is combined with the advantage of face detection
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