'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
The 2013 Boston Marathon bombing represents a case where automatic facial biometrics tools could have proven invaluable to law enforcement officials, yet the lack of ro-bustness of current tools in unstructured environments lim-ited their utility. In this work, we focus on complications that confound face detection algorithms. We first present a simple multi-pose generalization of the Viola-Jones al-gorithm. Our results on the Face Detection Data set and Benchmark (FDDB) show that it makes a significant im-provement over the state of the art for published algorithms. Conversely, our experiments demonstrate that the improve-ments attained by accommodating multiple poses can be negligible compared to the gains yielded by normalizing scores and using the most appropriate classifier for uncon-trolled data. We conclude with a qualitative evaluation of the proposed algorithm on publicly available images of the Boston Marathon crowds. Although the results of our evalu-ations are encouraging, they confirm that there is still room for improvement in terms of robustness to out-of-plane ro-tation, blur and occlusion. 1
