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Feature Engineering Techniques to Improve Identification Accuracy for Offline Signature Case-Bases
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Author(s): Shisna Sanyal (Jadavpur University, India), Anindta Desarkar (Jadavpur University, India), Uttam Kumar Das (Tata Consultancy Services, India)and Chitrita Chaudhuri (Jadavpur University, India)
Copyright: 2021
Volume: 7
Issue: 1
Pages: 19
Source title:
International Journal of Rough Sets and Data Analysis (IJRSDA)
Editor(s)-in-Chief: Parikshit Narendra Mahalle (Department of Artificial Intelligence and Data Science, Bansilal Ramnath Agarwal Charitable Trust's, Vishwakarma Institute of Information Technology, India)
DOI: 10.4018/IJRSDA.20210101.oa1
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Abstract
Handwritten signatures have been widely acclaimed for personal identification viability in educated human society. But, the astronomical growth of population in recent years warrant developing mechanized systems to remove the tedium and bias associated with manual checking. Here the proposed system, performing identification with Nearest Neighbor matching between offline signature images collected temporally. The raw images and their extracted features are preserved using Case Based Reasoning and Feature Engineering principles. Image patterns are captured through standard global and local features, along with some profitable indigenously developed features. Outlier feature values, on detection, are automatically replaced by their nearest statistically determined limit values. Search space reduction possibilities within the case base are probed on a few selected key features, applying Hierarchical clustering and Dendogram representation. Signature identification accuracy is found promising when compared with other machine learning techniques and a few existing well known approaches.
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