  Benefiting from the advance of deep convolutional neural network approaches
(CNNs), many face detection algorithms have achieved state-of-the-art
performance in terms of accuracy and very high speed in unconstrained
applications. However, due to the lack of public datasets and due to the
variation of the orientation of face images, the complex background and
lighting, defocus and the varying illumination of camera captured images, face
detection on identity documents under unconstrained environments has not been
sufficiently studied. To address this problem more efficiently, we survey three
state-of-the-art face detection methods based on general images, i.e.
Cascade-CNN, MTCNN and PCN, for face detection in camera captured images of
identity documents, given different image quality assessments. For that, The
MIDV-500 dataset, which is the largest and most challenging dataset for
identity documents, is used to evaluate the three methods. The evaluation
results show the performance and the limitations of the current methods for
face detection on identity documents under the wild complex environments. These
results show that the face detection task in camera captured images of identity
documents is challenging, providing a space to improve in the future works.
