RankSVM for Offline Signature Verification Signature verification systems suffer from imbalanced learning, which imposes strict requirements on classifiers. The standard classification approaches, such as SVM, often degrade the performance for imbalanced data or require additional parameters for data balancing. In this study, as a new approach for signature verification, we use RankSVM as the writerdependent classifiers, which theoretically guarantees the generalization performance for imbalanced data. To investigate the ability of RankSVM for solving imbalanced learning problems in signature verification tasks, the extensive experiments are conducted on bitmaps of GPDS150, GPDS300, GPDS600, and GPDS1000 datasets and deep features of GPDS960 dataset. The experimental results demonstrate that the RankSVMbased approach obtains a nearly equivalent performance with the stateoftheart method on deep features of the GPDS960 dataset, and achieves significantly better performance than standardSVMbased approach on bitmaps of GPDS150, GPDS300, GPDS600, and GPDS1000 datasets.

Capturing Micro Deformations from Pooling Layers for Offline Signature Verification 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 maxpooling 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 writerdependent classifiers for each user to build the signature verification system. The extensive experimental results on GPDS150, GPDS300, GPDS1000, GPDS2000, and GPDS5000 datasets demonstrate that the proposed method can discriminate the genuine signatures and their corresponding skilled forgeries well and achieve stateoftheart results on these datasets.