Static Signature Recognition System for User Authentication Based Two Level Cog, Hough Transform and Neural Network

This paper propose signature recognition system based on centre of gravity,hough transform and neural network for offline signature. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem.. In this paper, signature is preprocessed through binarization, cutting edges and thinning which provides more accurate platform for feature extraction methods. We have computed centre of gravity in two level by considering centre of gravity of all the characters separately instead of taking one common centre of gravity for entire signature and finally we would be able to built a system for signature recognition by taking mean values of all the centre of gravity values of various characters present in the signature. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. KEYWORD: Static, dynamic, edge detection, cog, back propagation, artificial neural network.