A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Hybrid architectures for efficient and secure face authentication in embedded systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Real-time face detection and lip feature extraction using field-programmable gate arrays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper describes the structure of real-time face detection hardware architecture for household robot applications. The proposed architecture is robust against illumination changes and operates at no less than 60 frames per second. It uses Modified Census Transform to obtain face characteristics robust against illumination changes. And the AdaBoost algorithm is adopted to learn and generate the characteristics of the face data, and finally detected the face using this data. This paper describes the hardware structure composed of Memory Interface, Image Scaler, MCT Generator, Candidate Detector, Confidence Mapper, Position Resizer, Data Grouper, and Overlay Processor, and then verified it using Virtex5 LX330 FPGA of Xilinx. Verification using the images from a camera showed that maximum 16 faces can be detected at the speed of maximum 30.