Biometric authentication techniques are in high demand for entrance monitoring and security systems. The techniques must be cheap, reliable and, simple. Handwritten signature verification satisfies these requirements. Signature Recognition is a very well known area in Biometrics. Signature of a person is one of the important biometric attribute, has been used for centuries as an authentication measure. In current era signatures are important in business, banking, legal application areas. With the tremendous developments in computer technology and advancements in programming platforms the field of biometrics has seen increments with leaps and bounds. In this book we have discussed an automatic off-line signature verification and forgery detection system based on clustering technique. This system uses the Vector Quantization, Walsh Coefficients, Geometric centers, Grid and Texture features as well as local and Global features of a static handwritten signature.

Morphology is the study of external features of an object. In image processing we use the morphological features based on the shape of the object, from this a template is created and used for matching purpose. We have applied this approach for the problem of handwritten signature recognition. The static signature recognition is simple and cost effective and as large number of commercial transactions are still done on paper, handwritten signatures are widely used for authorization of the documents. Hence the static signature recognition is an important aspect of document authorization using biometric authentication. Static signature is captured from the paper using image scanner, we have only static information such as shape, strokes, orientation available with us. We use techniques based on morphological operations such as erosion, dilation to generate the template and measure the authenticity of the signature.

Biometric Authentication Systems have become inevitable components of modern day security. In this book we have discussed fingerprint, palmprint, finger-knuckle print, face, iris, dynamic signature, keystroke dynamics and their multimodal implementations. In physiological category fingerprints, palmprints & finger-knuckle prints, face & iris are discussed. Their unimodal as well as multimodal implementations are presented. In case of behavioral biometrics dynamic signature & keystroke dynamics for biometric authentication have been investigated. Digital signal & image processing based techniques such as Kekre Wavelets, Kekre's Codebook Generation Algorithms and their variants are used for feature vector generation and recognition purpose. Multimodal systems consisting of the combinations of above mentioned biometric traits are presented. Multi-algorithmic, Multi-instance implementations are presented in this book. A new architecture called as hybrid multimodal system is also presented in this book. This book will be very much helpful for the people who are working in the biometric authentication domain.