Deep Fuse OSV: Online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture

Abstract

Online signature verification (OSV) is a widely utilised technique in the medical, e-commerce and m-commerce applications to lawfully bind the user. These high-speed systems demand faster writer verification with a limited amount of information along with restrictions on training and storage cost. This study makes two major contributions: (i) A competent feature fusion technique in which traditional statistical-based features are fused with deep representations from a convolutional auto-encoder; and (ii) a hybrid architecture combining depth-wise separable convolution neural network (DWSCNN) and long short term memory (LSTM) network delivering state-of-the-art performance for OSV is proposed. DWSCNN is utilised for extracting deep feature representations and LSTM is competent in learning long term dependencies of stroke points of a signature. This hybrid combination accomplishes better classification accuracy (lower error rates) even with one-shot learning, i.e. achieving higher classification accuracies with only one training signature sample per user. The authors have extensively evaluated their model using three widely used datasets MCYT-100, SVC and SUSIG. These exhaustive experimental studies confirm that the DeepFuseOSV framework results in the state-of-the-art outcome by achieving an equal error rate (EER) of 13.26, 2.58, 0.07% in Skilled 1, Skilled 10 and Random 10 categories of MCYT-100, respectively, 7.71% in Skilled 1 category of SVC, 1.70% in Random 1 category of SUSIG.