Face Detection in Camera Captured Images of Identity Documents under Challenging Conditions
This work addresses face detection in identity documents for applications like verification, but it is incremental as it applies existing methods to a new dataset without proposing a novel solution.
The paper evaluated three existing face detection methods (Cascade-CNN, MTCNN, PCN) on the MIDV-500 dataset for detecting faces in camera-captured identity documents under challenging conditions like orientation, lighting, and background, finding that the task remains difficult with room for improvement.
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.