Despite the maturity of face detection research, it re- mains difficult to compare different algorithms for face de- tection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detec- tion algorithms do not capture some aspects of face appear- ances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two rigorous and precise methods for evaluat- ing the performance of face detection algorithms. We report results of several standard algorithms on the new bench- mark.