Invariant Local Features for Face Detection
نویسنده
چکیده
In the domain of object recognition, the SIFT feature [1] is known to be a very successful local invariant feature. The performance of the recognition task using SIFT features is very robust and also can be done in real-time. This project present an approach that adopt the SIFT feature for the task of face detection. A feature database is created for the detection of generic face features and a model fitting algorithm is used to resolve the scale, orientation and position of the faces in images. Because of the advantages brought by the SIFT features, the system can easily detect faces of any scale and rotation. A test is done using 16 images with 200 faces in total. The result shows a detection rate of 72.5% with 46 false detections.
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