TY - GEN
T1 - Deep Learning-Based Automated Detection of Inappropriate Face Image Attributes for ID Documents
AU - Mazandarani, Amineh
AU - Amaral, Pedro Miguel Figueiredo
AU - da Fonseca Pinto, Paulo
AU - Shamoushaki, Seyed Jafar Hosseini
N1 - Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.
PY - 2021
Y1 - 2021
N2 - A face photo forms a fundamental element of almost every identity document such as national ID cards, passports, etc. The governmental agencies issuing such documents may set slightly different requirements for a face image to be acceptable. Nevertheless, some are too critical to avoid, such as mouth closedness, eyes openness and no veil-over-face. In this paper, we aim to address the problem of fully automating the inspection of these 3 characteristics, thereby enabling the face capturing devices to determine, as soon as a face image is taken, if any of them is invalid or not. To accomplish this, we propose a deep learning-based approach by defining model architectures that are lightweight enough to enable real-time inference on resource-constrained devices with a particular focus on prediction accuracy. Lastly, we showcase the performance and efficiency of our approach, which is found to surpass two well-known off-the-shelf solutions in terms of overall precision.
AB - A face photo forms a fundamental element of almost every identity document such as national ID cards, passports, etc. The governmental agencies issuing such documents may set slightly different requirements for a face image to be acceptable. Nevertheless, some are too critical to avoid, such as mouth closedness, eyes openness and no veil-over-face. In this paper, we aim to address the problem of fully automating the inspection of these 3 characteristics, thereby enabling the face capturing devices to determine, as soon as a face image is taken, if any of them is invalid or not. To accomplish this, we propose a deep learning-based approach by defining model architectures that are lightweight enough to enable real-time inference on resource-constrained devices with a particular focus on prediction accuracy. Lastly, we showcase the performance and efficiency of our approach, which is found to surpass two well-known off-the-shelf solutions in terms of overall precision.
KW - Binary classification
KW - Deep learning
KW - Face detection
KW - Image quality verification
UR - http://www.scopus.com/inward/record.url?scp=85111994051&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78288-7_23
DO - 10.1007/978-3-030-78288-7_23
M3 - Conference contribution
AN - SCOPUS:85111994051
SN - 978-3-030-78287-0
T3 - IFIP Advances in Information and Communication Technology
SP - 243
EP - 253
BT - Technological Innovation for Applied AI Systems - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Ferreira, Pedro
A2 - Brito, Guilherme
PB - Springer
CY - Cham
T2 - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021
Y2 - 7 July 2021 through 9 July 2021
ER -