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This project uses an offline learning algorithm to get a highly efficient classifier for online image retrieval. The boosting algorithm is adopted for the learning process. It chooses a small number (10 in this project) of highly selective features from a very large feature set (there are totally over 45,000 features in the set in this project) and combine(More)
The topic covered in this project is face detection in a single image, which means no temporal information can be utilized from consecutive frames in a video. The task of face detection in a single image is to detect the existence of human face in an arbitrary image and show the location if faces exist. This is nontrivial because there are several(More)
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(More)
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