Average Intensity Sign (AIS) Feature based Offline Signature Verification for Forgery Detection using Machine Learning

The human offline signs are the utmost extensively approved biometric verification procedures in colleges, industries, banking and various domain due to its easiness and exclusivity. Numerous computerized technologies have been established to predict the human offline signature for forgery detection. Because, the offline biometric identification system has expanded a histrionic movement. This paper Suggests a technique for offline sign identification system using image processing techniques. The proposed offline sign identification system has four stages. In the first stage, the pre-processing of human sig can be done. The pre-processing stage can be done by means of image processing concepts which includes color conversion and next smoothening technique can be done using Gaussian filter. And followed by noise removal method can be done using morphological operation. The second stage of proposed system is sign detection using various edge detection algorithm. In the third stage, extract the Average Intensity Sign (AIS) featurefrom detected sign image. Finally, signature verification system can be utilized for forgery detection using machine learning SVM techniques. The proposed sign identification method demonstrations that the real time experimental output has extreme achievement ratio. This proposed algorithm yields average accuracyof 98.91% in SVM (RBF) in 36 AIS features when associated to an SVM(polynomial) classifier.