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.
Highlights
From ancient times, handwritten signature is the most well-known biometric characteristic for appropriate identification of a person or to authenticate a document
The objective of our research is to build a classifier which helps to detect the identity of a person, where training begins by comparing presented signature with each case or person preserved in the base
Three datasets are used in the experiment where the major part in each set are utilized for model building and rest of the set is treated as test dataset on which signature identification accuracy is calculated
Summary
Handwritten signature is the most well-known biometric characteristic for appropriate identification of a person or to authenticate a document. The mode of collection is the easiest and cheapest For these reasons, it has been one of the most popular techniques favored so far. Instance based classification techniques are utilized to identify a person by comparing distances between feature vectors representing offline handwritten signature images of that person. Clustering involves finding a structure in a collection of unlabeled data, leading to discovery of a new set of categories intrinsically, presented in the research by Nirmala et al (Nirmala & Saravanan, 2014). The trees can be formed either using a Top-to–bottom (Divisive) approach or a Bottom-Up (Agglomerative) approach using a special structure known as Dendrogram. The technique adopted in this research utilizes the Agglomerative approach
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