Synthetic data generation technique in Signer-independent sign language recognition
Pattern Recognition Letters
Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis
AmI '09 Proceedings of the European Conference on Ambient Intelligence
A semi-supervised support vector machine based algorithm for face recognition
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
AdaBoost-based face detection for embedded systems
Computer Vision and Image Understanding
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
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Over the past ten years, face detection has been thoroughly studied in computer vision research for its interesting applications. However, all of the state-of-the-art statistical methods suffer from the data collection for training a classifier. This paper presents a self-adaptive genetic algorithm (GA)-based method to swell face database through re-sampling from the existing faces. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. To verify the generalization capability of the proposed method, we also use the expanded database to train an Adaboost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods.