TY - GEN
T1 - An efficient dense network for semantic segmentation of eyes images captured with virtual reality lens
AU - Valenzuela, Andres
AU - Arellano, Claudia
AU - Tapia, Juan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Eye-tracking and Gaze estimation are difficult tasks that may be used for several applications including human-computer interfaces, salience detection and Virtual reality amongst others. This paper presents a segmentation algorithm based on deep learning that efficiently discriminates pupils, iris, and sclera from the background in images captured using a Virtual Reality lens. A light network called DensetNet10 trained from scratch is proposed. It contains fewer parameters than traditional architectures based on DenseNet which allows it to be used in mobile device applications. Experiments show that this network achieved higher IOU rates when comparing with DensetNet56-67-103 and DeeplabV3+ models in the Facebook database. Furthermore, this method reached 8th place in The Facebook semantic segmentation challenge with 0.94293 mean IOU and 202.084 parameters with a final score of 0.97147.
AB - Eye-tracking and Gaze estimation are difficult tasks that may be used for several applications including human-computer interfaces, salience detection and Virtual reality amongst others. This paper presents a segmentation algorithm based on deep learning that efficiently discriminates pupils, iris, and sclera from the background in images captured using a Virtual Reality lens. A light network called DensetNet10 trained from scratch is proposed. It contains fewer parameters than traditional architectures based on DenseNet which allows it to be used in mobile device applications. Experiments show that this network achieved higher IOU rates when comparing with DensetNet56-67-103 and DeeplabV3+ models in the Facebook database. Furthermore, this method reached 8th place in The Facebook semantic segmentation challenge with 0.94293 mean IOU and 202.084 parameters with a final score of 0.97147.
KW - Biometrics
KW - Facebook Challengue
KW - Semantic Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85084850623&partnerID=8YFLogxK
U2 - 10.1109/SITIS.2019.00017
DO - 10.1109/SITIS.2019.00017
M3 - Conference contribution
AN - SCOPUS:85084850623
T3 - Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019
SP - 28
EP - 34
BT - Proceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019
A2 - Yetongnon, Kokou
A2 - Dipanda, Albert
A2 - Sanniti di Baja, Gabriella
A2 - Gallo, Luigi
A2 - Chbeir, Richard
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019
Y2 - 26 November 2019 through 29 November 2019
ER -