(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 1 Off-line Handwritten Signature Recognition Using Wavelet Neural Network Mayada Tarek 1 Computer Science Department, Faculty of Computers and Information Sciences, Mansoura, Egypt Taher Hamza Computer Science Department, Faculty of Computers and Information Sciences, Mansoura, Egypt Elsayed Radwan Computer Science Department, Faculty of Computers and Information Sciences, Mansoura, Egypt Abstract ـــ ـAutomatic signature verification is a well- established and an active area for research with numerous applications such as bank check verification, ATM access, etc. Most off-Line signature verification systems depend on pixels intensity in feature extraction process which is sensitive to noise and any scale or rotation process on signature image. This paper proposes an off-line handwritten signature recognition system using Discrete Wavelet Transform as feature extraction technique to extract wavelet energy values from signature image without any dependency of image pixels intensity. Since Discrete Wavelet Transform suffers from down-sample process, Wavelet Neural Network is used as a classifier to solve this problem. A comparative study will be illustrated between the proposed combination system and pervious off-line handwritten signature recognition systems. Conclusions will be appeared and future work is proposed. Keywords-Discrete Wavelet Transform (DWT); Wavelet Energy; Wavelet Neural Network (WNN); Off-line Handwritten Signature. I. INTRODUCTION In the field of personal identification, two types of biometrics means can be considered; first, physiological biometrics, which involves data derived from the direct measurement of some part of the human body; for- example fingerprint-, face-, palm print-, retina-based verification. Second, behavioural biometrics, which involves data derived from an action taken by a person, or indirectly measures characteristics of the human body; for-example: speech-, keystroke dynamics and signature-based verification [1]. In the last few decades, researchers have made great efforts on off-line signature verification [1] for- example; using the statistics of high grey-level pixels to identify pseudo-dynamic characteristics of signatures; developing technique based on global and grid features in conjunction with a simple Euclidean distance classifier; proposing a system for off-line signature verification consists of four subsystems based on geometric features, moment representations, envelope characteristics and wavelet features; applying wavelet on signature verification [2,3,4,5]. Although these methods achieved a good results, they still suffer from the exchangeability of signature rotation and the distinguish-ability of person signature size. Most of these feature extraction methods depend on signature shape or pixels intensity in specific region of signature. However, pixels' intensity are sensitive to noise and also the signature shape may vary according to translation, rotation and scale variations of signature image [6]. Two types of feature can be extracted from signature image; first, global features which are extracted from the whole signature, including block codes [7]; second, local features which are calculated to describe the geometrical and topological characteristics of local segments [8]. Because of the absence of dynamic information in offline verification system, global features extraction are most appropriate [9]. One of the most appropriate global features extraction techniques is wavelet transform, since it extracts time-frequency wavelet coefficients from the signature image [8]. Wavelet Transform is especially suitable for processing an off-line signature image where most details could be hardly represented by functions, but could be matched by the various versions of the mother wavelet with various translations and dilations [10]. Also, wavelet transform is invariant to translation, rotation and scale of the image. Because of the advantage of wavelet transform, this paper uses it in feature extraction stage. Since one of problems that face wavelet is the huge size of its coefficients, statistical model can be introduced to represent them. This paper uses wavelet energy as statistical model to represent all wavelet coefficients in efficient way. Another problem is down-sample process which can lose some important extracted feature from signature image[11]. This paper proposes a Wavelet Neural Network (WNN) technique for off-line signature recognition to overcome the disadvantages of Discrete Wavelet Transform (DWT) down-sample process. 1 Corresponding Author Mail: mayaatarek@yahoo.com Tel : 020108631688