A new wrapper feature selection method for language-invariant offline signature verification. (30th December 2021) - Banerjee, Debanshu; Chatterjee, Bitanu; Bhowal, Pratik; Bhattacharyya, Trinav; Malakar, Samir; Sarkar, Ram
Abstract: Among various biometric systems, an offline signature verification system has been widely used in all fields such as in banks, educational institutes, legal procedures and, criminal investigation where authentication and verification are utmost required. Despite the popularity of the online signature verification system, its offline counterpart still has great importance in developing countries, especially in rural areas, where easy availability of smart devices along with fast internet connection is not available. In this work, we have developed a language invariant offline signature verification model which is almost equally applicable for both writer dependent and writer independent scenarios. At first, an offline signature is collected as an image, following which a corresponding signal is generated using singular value decomposition. Then four different kinds of features namely, statistical, shape-based, similarity-based, and frequency-based are extracted from the transformed signal of the signature image. Next, to reduce the feature dimension, we have designed a novel wrapper feature selection method based on Red Deer Algorithm, a recently proposed meta-heuristic method, to keep only the relevant features to be used during signature authentication and verification process. Finally, a confidence score from the Naïve Bayes classifier has been used to perform the authentication and verification process. Our model has been evaluated on CEDAR (English), UTSig (Persian), Sigcomp 2011 Dutch, Sigcomp 2011 Chinese, and SigWIcomp 2015 Bengali signature datasets. Obtained results confirm that the proposed model can outperform many of its predecessors. Highlights: Developed a language invariant signature verification framework. Converted signature images to signals utilizing singular value decomposition. Extracted features from signals rather than images unlike other methods. Used Red Deer Algorithm as a meta-heuristic wrapper feature selection method. Achieved satisfactory performance as compared to some state-of-the-art methods.