Syncretize AdaBoost Learning and Heuristic Search to Select Features for Gender Classification of Frontal Facial Images
Qiang Liu · Jisuanji gongcheng · 2007
This paper presents a method based on AdaBoost to identify the sex of a person from a low resolution grayscale picture of their frontal facial images.A heuristic search algorithm is used within the AdaBoost framework to find new features providing better classifiers.The experiments result of gender classification with the method presented in this paper indicate that the method is extremely fast and achieves over 93% accuracy with less than 500 pixel comparisons operations,these match the accuracies of the SVM-based classifiers which the best classifiers published to date.