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Biometrics, the word not only generates a spark of interest but it
is also an important aspect to trace human expressions. Signature,
an expression of this kind portrays a uniqueness which can be
captured thus it becomes vital. Signature is a mark of authenticity
which can be tampered by observing carefully. Dynamic Signature
Recognition is one of the highly accurate biometric traits. Live
signature of the person is captured, hence it is possible to have
dynamic characteristics of signature for matching purpose. The
signature captured by digitizer gives information about dynamic
nature of signature and pressure applied while signing. This book
is discussing use of Dynamic Signature Recognition using Hybrid
wavelets. The technique is fast and gives good accuracy and tested
in real time. Hybrid Wavelets are used for feature vector
extraction, this is done by multiresolution analysis of the dynamic
signatures. Wavelet energy based feature vector is generated and
this is to be used for the matching of the signatures. The typical
features set consists of x, y, z co-ordinates, Pressure, Azimuth
and Altitude points. For performance evaluation we are using
Conventional FAR-FRR analysis.
In this book a video signal processing algorithm is proposed which
is Modified H.264/AVC as a solution for Bandwidth saving through
LBR (Low Bit Rate) applications to ensure acceptable video quality.
Based on this concept, where the intraframe pictures are hybrid
wavelet-based coded and the interframe pictures are coded with a
Modified H.264/AVC encoder and decoder is proposed. Use of Modified
H.264/AVC algorithm shows that this proposed hybrid encoder and
decoder not only retains the same quality of the video but also
lowers down the bit rate of the video considerably. AES based
encryption and decryption system is proposed for added security for
transmission and reception. The proposed algorithm is characterized
by their efficient compression ratio, good PSNR, MSE results and
lower bit rate as well as its efficient implementation in MATLAB.
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.
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