In the field of face detection

how to accurately detect and locate faces in the images is a key problem. In order to improve the performance of face detector

the common approach is to add more training datasets or use more robust facial feature representations. Yet

one of the foundational tasks in training face detector is the annotation of the face bounding boxes for the training images. Should the face bounding box include ear information? What is the corresponding influence on the performance of profile face detection? This is an uninvestigated issue. Our experimental results show that

when using profile face datasets and the faces bounding include ear to train the face detector

the accuracy of profile face detector based on DPM (the Deformable Parts Model) decreases by 1. 9%

yet

the recall increases by 6. 3%. In the case of ViolaJones and Fast R-CNN

the improvements are significant

the corresponding accuracy increases by 6. 8% and 4. 4%

and recall increases by 14. 9% and 12. 9%

respectively. This reveals that

when training profile face detection model with the ear information

it can significantly improve the recall of profile face detection.