Offline signature verification based on contourlet transform and textural features using HMM

Automatic offline signature verification and recognition is becoming essential in personal authentication. In this paper, we propose a transform domain offline signature verification system based on contourlet transform, directional features and Hidden Markov Model (HMM) as classifier. The signature image is preprocessed for noise removal and a two level contourlet transform is applied to get feature vector. The textural features are computed and concatenated with coefficients of contourlet transform to form the final feature vector. HTK tool with HMM classifier is used for classification. The parameters of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are calculated for GPDS-960 database. It is found that the parameters of FRR and FAR are improved compared to the existing algorithms.