Eigen Value Based Features for Offline Handwritten Signature Verification Using Neural Network Approach

Handwritten Signature is primary means for authentication and identification process. In this paper, we have extracted the features based on Eigen values techniques. Eigen values are computed from upper and lower envelope, envelopes represents the contours of the signature. In proposed work we are using GPDS Synthetic Signature Corpus database. Significant features are extracted from signatures which consist of large and small Eigen values computed from upper envelope and lower envelope and its union values. Both the envelopes are fused by performing union operation and their covariance is computed. The difference and ratios of high and low points of both the envelopes are computed. Lastly average values of both the envelopes are obtained. These features set are coupled with neural network pattern recognition classifier that lead to 98.1% of accuracy and FAR 1.9%.