Robust Real-Time Face Detection
International Journal of Computer Vision
Facial feature detection using Haar classifiers
Journal of Computing Sciences in Colleges
Local Rank Patterns --- Novel Features for Rapid Object Detection
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
GP-GPU Implementation of the "Local Rank Differences" Image Feature
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
A Flexible Parallel Hardware Architecture for AdaBoost-Based Real-Time Object Detection
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Learning multi-scale block local binary patterns for face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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The paper presents a new concept of creating an energy and computation effective AdaBoost classifier systems. The presented method is mainly novel in the way how it divides and accelerates an AdaBoost classifier into two parts -- a pre-processing and a post-processing unit. Pre-processing unit is designed to process a major part of the computational operations of the AdaBoost algorithm but it is also helps in an energy savings.