TY - GEN
T1 - Capturing micro deformations from pooling layers for offline signature verification
AU - Zheng, Yuchen
AU - Ohyama, Wataru
AU - Iwana, Brian Kenji
AU - Uchida, Seiichi
N1 - Funding Information:
This work was supported by JSPS KAKENHI (Grant Number JP18K11373 and JP17H06100) and China Scholarship Council (Grant Number 201706330078).
PY - 2019/9
Y1 - 2019/9
N2 - In this paper, we propose a novel Convolutional Neural Network (CNN) based method that extracts the location information (displacement features) of the maximums in the max-pooling operation and fuses it with the pooling features to capture the micro deformations between the genuine signatures and skilled forgeries as a feature extraction procedure. After the feature extraction procedure, we apply support vector machines (SVMs) as writer-dependent classifiers for each user to build the signature verification system. The extensive experimental results on GPDS-150, GPDS-300, GPDS-1000, GPDS-2000, and GPDS-5000 datasets demonstrate that the proposed method can discriminate the genuine signatures and their corresponding skilled forgeries well and achieve state-of-the-art results on these datasets.
AB - In this paper, we propose a novel Convolutional Neural Network (CNN) based method that extracts the location information (displacement features) of the maximums in the max-pooling operation and fuses it with the pooling features to capture the micro deformations between the genuine signatures and skilled forgeries as a feature extraction procedure. After the feature extraction procedure, we apply support vector machines (SVMs) as writer-dependent classifiers for each user to build the signature verification system. The extensive experimental results on GPDS-150, GPDS-300, GPDS-1000, GPDS-2000, and GPDS-5000 datasets demonstrate that the proposed method can discriminate the genuine signatures and their corresponding skilled forgeries well and achieve state-of-the-art results on these datasets.
UR - http://www.scopus.com/inward/record.url?scp=85079891614&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079891614&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2019.00180
DO - 10.1109/ICDAR.2019.00180
M3 - Conference contribution
AN - SCOPUS:85079891614
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1111
EP - 1116
BT - Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
PB - IEEE Computer Society
T2 - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
Y2 - 20 September 2019 through 25 September 2019
ER -