OSPCV: Off-line Signature Verification using Principal Component Variances

Signature verification system is always the most sought after biometric verification system. Being a behavioral biometric feature which can always be imitated, the researcher faces a challenge in designing such a system, which has to counter intrapersonal and interpersonal variations. The paper presents a comprehensive way of off-line signature verification based on two features namely, the pixel density and the centre of gravity distance. The data processing consists of two parallel processes namely Signature training and Test signature analysis. Signature training involves extraction of features from the samples of database and Test signature analysis involves extraction of features from test signature and it's comparison with those of trained values from database. The features are analyzed using Principal Component Analysis (PCA). The proposed work provides a feasible result and a notable improvement over the existing systems. I. Introduction Identification of individuals is a very important aspect of security. The identification techniques opted may vary according to conveniences and requirements. The identification may be carried out by identity cards, pin codes, smart cards etc., but these are easily misused. A better way of individual identification is on a biological scale which is biometric verification. The biometric verification involves identification of individuals based on physiological and behavioral features. The physiological features include iris, face, finger print, DNA etc., and the behavioral features include voice, signature, gait which are unique to a person. Signatures have been a primary method of identification of a person in all fields for purposes such as credit cards, contract agreements, cheques, wills, and other important documents. Thus a signature is widely used behavioral biometric for identifying a person. In day to day life millions of signatures need to be verified; this tends to be impossible by visual inspection and therefore an automated system is necessary for determining the authenticity of the signature. Several decades have witnessed intense research in the field of signature verification, especially in the Off-line signature verification. Signature verification is a process of discriminating between genuine and forged set of handwritten signatures and it is a difficult task as the signatures are a result of the physical and psychological status of an individual process. Several techniques including different features of signature have been developed for the purpose of signature verification. The signature verification can be of two main types, the on-line or dynamic verification system and the off-line or static verification system. The main steps of signature verification system are preprocessing, thinning, feature extraction, and verification. Feature selection and extraction are fundamental processes in any verification system. The features used in the off-line signature verification are signature image area, length to width ratio, geometric centers, angle and distance of a pixel from a reference point, signature height and width. On-line signature verification system has features such as pen pressure, tilt, velocity, number of strokes required etc. The feature sets provide an ambiguous performance; hence signature verification has become a challenging task. Off-line verification of signatures is done by considering an image of the signature which is obtained by using a scanner or a camera and extracting its features. Since the signature is scanned from a paper, it is considered as a static image. Off-line signature verification is difficult due to limited amount of features which can be extracted and the absence of dynamic features. Thus Off-line global and local features are extracted from the original signature and fed into the system and are later compared with test signatures using various