An efficient open system for offline handwritten signature identification based on curvelet transform and one-class principal component analysis

Offline handwritten signature identification has received less attention in comparison with the offline signature verification, despite its crucial applications such as in law-enforcements, automatic bank check and historical documents processing. In this paper, an Open Handwritten Signature Identification System (OHSIS) is proposed by using conjointly the Curvelet Transform (CT) and the One-Class classifier based on Principal Component Analysis (OC-PCA). CT is explored for feature generation due to its efficient characterization of curves contained into the local orientations within the signature image. While, OC-PCA is used for its effectiveness to absorb the high feature size generated by the CT and allows achieving at the same time an open system. Then, in order to improve the robustness of the OHSIS when few reference signatures are available, a new combination approach based on Choquet fuzzy integral is proposed to combine multiple individual OHSISs. Furthermore, a designing protocol with limited number of writers and reference signatures is proposed to perform a parameter-independent OHSIS. Experimental results conducted on standard CEDAR and GPDS handwritten signature datasets report 97.99% and 94.96% correct identification rate, respectively, which highlights the effectiveness of the proposed OHSIS since it can comfortably outperform the state-of-the-art when using few reference signatures. (C) 2017 Elsevier B.V. All rights reserved.