Abstract
This is a study of applying Case Based Reasoning techniques to identify a person using offline signature images. Classification related to proper identification is achieved by comparing distance measures of new test cases with existing cases within the base. The patterns pertaining to the images are captured by fusing some standard global features with some indigenously developed local feature sets. Outlier values in both these sets are handled to maintain statistical tolerable limits. The effect of outlier handling within feature values is found to enhance identification accuracy for two standards and one indigenously collected offline signature sets utilized in the experimental phase.
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The project is supported by ‘Mobile Computing and Innovation Applications’ funded by UGC UPE II of Jadavpur University, India.
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Das, U.K., Sanyal, S., De Sarkar, A., Chaudhuri, C. (2020). Enhancement of Identification Accuracy by Handling Outlier Feature Values Within a Signature Case Base. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_16
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DOI: https://doi.org/10.1007/978-981-13-8676-3_16
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