The effectiveness of face detection algorithms in unconstrained crowd scenes

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 robustness of current tools in unstructured environments limited 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 algorithm. Our results on the Face Detection Data set and Benchmark (FDDB) show that it makes a significant improvement over the state of the art for published algorithms. Conversely, our experiments demonstrate that the improvements attained by accommodating multiple poses can be negligible compared to the gains yielded by normalizing scores and using the most appropriate classifier for uncontrolled 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 evaluations are encouraging, they confirm that there is still room for improvement in terms of robustness to out-of-plane rotation, blur and occlusion.