Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Face Pose Discrimination Using Support Vector Machines (SVM)
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Pose Estimation for Central Catadioptric Systems: An Analytical Approach
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, the framework and implementation of a real time multi-scale face detection system using appearance-based learning method and multi-pose hybrid learning approach. Multiple scale and pose based object detection is attractive since it could accumulate the face models by autonomous learning process. Face image, however, can be approximated even though it is represented with many scales. A real time face detection determines the location and size of each human face(if any) in an input image. Detecting varying human face in video frames is an important task in many computer vision applications such as human-computer interface. The face detection proposed in this paper employs hybrid learning approach and statistical method. We employ FuzzyART and RBF Network and Mahalanobis distance. We achieve a very encouraging experimental results.