Authors:
Daniel Canedo
;
José Vieira
;
António Gonçalves
and
António Neves
Affiliation:
IEETA, DETI, LASI, University of Aveiro, 3810-193 Aveiro, Portugal
Keyword(s):
Computer Vision, Face Verification, Deep Learning, Identity Photos, Photo Management.
Abstract:
The recent developments in Deep Learning and Computer Vision algorithms allow the automation of several tasks which up until that point required the allocation of considerable human resources. One task that is getting behind the recent developments is the management of identity photos for institutional staff directories because it deals with sensitive information, namely the association of a photo to a person. The main objective of this work is to give a contribution to the automation of this process. This paper proposes several image processing algorithms to validate the submission of a new personal photo to the system, such as face detection, face recognition, face cropping, image quality assessment, head pose estimation, gaze estimation, blink detection, and sunglasses detection. These algorithms allow the verification of the submitted photo according to some predefined criteria. Generally, these criteria revolve around verifying if the face on the photo is of the person that is u
pdating their photo, forcing the face to be centered on the image, verifying if the photo has visually good quality, among others. A use-case is presented based on the integration of the developed algorithms as a web-service to be used by the image directory system of the University of Aveiro. The proposed service is called every time a collaborator tries to update their personal photo and the result of the analysis determines if the photo is valid and the personal profile is updated. The system is already in production and the results that are being obtained are very satisfactory, according to the feedback of the users. Regarding the individual algorithms, the experimental results obtained range from 92% to 100% of accuracy, depending on the image processing algorithm being tested.
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