Online signature verification using deep learning based aggregated convolutional feature representation

Abstract

Handwritten signature for the identification and authentication of an individual has been widely used in the biometric systems. Due to the intra-class and inter-class variabilities, signature verification has become one of the most challenging problem in the biometric technology. Furthermore, the offline handwritten signature can be forged by the skilled persons due to its static nature. Therefore, in this paper a deep learning-based method using convolutional neural network (CNN) for online signature verification has been developed. Different values of the convolutional kernels such as 1×1, 3×3 and 5×5 are used to extract the discriminative features at multi-scales. The features of the initial and middle layers of the CNN are combined to create more powerful features. An up-sampling method with bilinear interpolation has been used to add the features of convolutional layers with different spatial dimensions. Both the addition and concatenation methods have been used to aggregate the convolutional features. A convolutional transpose method is applied to increase the depth of the convolutional layers while performing an addition operation on the layers with different depths. Finally, the concatenated features are passed to the fully connected layers for high-level feature extraction and classification. To evaluate the performance of the proposed method, an android application was developed where; a custom database of 985 online signatures collected from 197 users has been created. The problem of inadequate training data for online signature verification has been addressed through the data augmentation method. The experimental results show that the deep aggregated convolutional feature representation method achieves an accuracy of 99.32% on the custom developed online signature database.