Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Face Detection on a Configurable Hardware System
FPL '00 Proceedings of the The Roadmap to Reconfigurable Computing, 10th International Workshop on Field-Programmable Logic and Applications
Robust Real-Time Face Detection
International Journal of Computer Vision
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection for automatic exposure control in handheld camera
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
A Parallel Architecture for Hardware Face Detection
ISVLSI '06 Proceedings of the IEEE Computer Society Annual Symposium on Emerging VLSI Technologies and Architectures
Parallelized Architecture of Multiple Classifiers for Face Detection
ASAP '09 Proceedings of the 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors
Fast and robust face detection on a parallel optimized architecture implemented on FPGA
IEEE Transactions on Circuits and Systems for Video Technology
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Recognizing faces in a crowd in real-time is a key feature which would significantly enhance Intelligent Surveillance Systems. Previously we proposed a high resolution smart camera system that can be used for crowd surveillance. The challenge is with the increasing speed and resolution of the image sensors, a fast and robust face detection system is required for real time operation. In this paper, we proposed a face detection architecture that is suitable to be implemented on a smart camera system. The face detection algorithm is based on a new weak classifier type that we called square patch feature. The targeted platform is a low cost Spartan-3 FPGA. From The simulation result shows that the proposed face detection architecture could speed up the equivalent software based face detector up to 12 times. Parallelizing the feature classification modules could improve the performance further.