Soft margin AdaBoost for face pose classification
Ying Guo, Geoff Poulton, Jiaming Li, Mark Hedley, Rong-Yu Qiao · 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). · 2003
The paper presents a new machine learning method to solve the pose estimation problem. The method is based on the soft margin AdaBoost (SMA) algorithm (Ratsch, G. et al., Machine Learning, vol.42, no.3, p.287-320, 2001). The AdaBoost algorithm has been used with great success as a high-level learning procedure to obtain strong classifiers from weak classifiers, but it tends to overfit in the presence of very noisy data. Recent studies show that a regularised AdaBoost algorithm, such as SMA, can achieve better results for noisy data. We propose two new techniques for classifying the image as frontal (face is within /spl plusmn/25/spl deg/) or profile; one is based on the original Adaboost algorithm, the other on SMA. It is shown that the SMA based technique is more robust than the one based on the original AdaBoost, and yields better results. All the techniques were trained and tested on four databases. Experimental results show that the classification error of the SMA method is less than 2% for suitable parameters, regardless of the conditions associated with the face. In addition, the method performs extremely well even when some facial features become partially or wholly occluded.