TY - GEN
T1 - Low-Resolution Face Recognition in Multi-person Indoor Environments Using Convolutional Neural Networks
AU - Lee, Greg C.
AU - Lee, Yu Che
AU - Chiang, Cheng Chieh
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Face recognition has been widely applied in many systems in our lives. These applications have reached good accuracies on face recognition tasks when face images can be captured with good quality, particularly when they have a high enough resolution. However, in an indoor environment, a surveillance camera often covers a wide area with multiple persons; this leads to only lower resolutions of face images are available in face recognition. This paper presents a face recognition approach for low resolution images using convolutional neural network (CNN) in a multi-person indoor environment. Our methods first detect face regions with the YOLOv3 approach and then recognize face images using the trained CNN model. Experiments are performed in an indoor classroom to capture face images with resolutions ranging from 20x20 to 70x70. Moreover, face images are extracted over 4 months to test the stability of our proposed face recognition model.
AB - Face recognition has been widely applied in many systems in our lives. These applications have reached good accuracies on face recognition tasks when face images can be captured with good quality, particularly when they have a high enough resolution. However, in an indoor environment, a surveillance camera often covers a wide area with multiple persons; this leads to only lower resolutions of face images are available in face recognition. This paper presents a face recognition approach for low resolution images using convolutional neural network (CNN) in a multi-person indoor environment. Our methods first detect face regions with the YOLOv3 approach and then recognize face images using the trained CNN model. Experiments are performed in an indoor classroom to capture face images with resolutions ranging from 20x20 to 70x70. Moreover, face images are extracted over 4 months to test the stability of our proposed face recognition model.
KW - convolutional neural network
KW - face detection
KW - face recognition
KW - low resolution face image
UR - http://www.scopus.com/inward/record.url?scp=85133921625&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133921625&partnerID=8YFLogxK
U2 - 10.1109/CSCI54926.2021.00313
DO - 10.1109/CSCI54926.2021.00313
M3 - Conference contribution
AN - SCOPUS:85133921625
T3 - Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021
SP - 1629
EP - 1633
BT - Proceedings - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Computational Science and Computational Intelligence, CSCI 2021
Y2 - 15 December 2021 through 17 December 2021
ER -