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Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.


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Journal Article
TL;DR: The main goal of this paper is to discuss the various techniques of speech recognition and study Hidden Markov Model of stochastic approach to develop voice based, user friendly interface software system.
Abstract: In today‟s world, Speech Recognition is very important and popular. Automatic Speech Recognition System consists of three phases: Preprocessing, Feature Extraction and Recognition. Speech recognition is the process of converting spoken words into text. In case of speech recognition the research followers are mainly using three different approaches namely Acoustic phonetic approach, Pattern recognition approach and Artificial intelligence approach. The main goal of this paper is to discuss the various techniques of speech recognition and study Hidden Markov Model of stochastic approach to develop voice based, user friendly interface software system.

11 citations

Book ChapterDOI
21 Oct 2011
TL;DR: Iris recognition is one of the most reliable and accurate biometrics that plays an important role in identification of individuals and nowadays biometric technology plays importantrole in public security and information security domains.
Abstract: Humans have distinctive and unique traits which can be used to distinguish them from other humans, acting as a form of identification. A number of traits characterising physiological or behavioral characteristics of human can be used for biometric identification. Basic physiological characteristics are face, facial thermograms, fingerprint, iris, retina, hand geometry, odour/scent. Voice, signature, typing rhythm, gait are related to behavioral characteristics. The critical attributes of these characteristics for reliably recognition are the variations of selected characteristic across the human population, uniqueness of these characteristics for each individual, their immutability over time (Jain et al.,1998). Human iris is the best characteristic when we consider these attributes. The texture of iris is complex, unique, and very stable throughout life. Iris patterns have a high degree of randomness in their structure. This is what makes them unique. The iris is a protected internal organ and it can be used as an identity document or a password offering a very high degree of identity assurance. Also the human iris is immutable over time. From one year of age until death, the patterns of the iris are relatively constant (Jain et al.,1998, Adler,1965). Because of uniqueness and immutability, iris recognition is one of accurate and reliable human identification technique. Nowadays biometrics technology plays important role in public security and information security domains. Iris recognition is one of the most reliable and accurate biometrics that plays an important role in identification of individuals. The iris recognition method deliver accurate results under varied environmental circumstances. Iris is the part between the pupil and the white sclera. The iris texture provides many minute characteristics such as freckles, coronas, stripes, furrows, crypts (Adler,1965). These visible characteristics are unique for each subject. Iris recognition process can be separated into these basic stages: iris capturing, preprocessing and recognition of the iris region. Each of these steps uses different algorithms. Pre-processing includes iris localization, normalization, and enhancement. In iris localization step, the detection of the inner (pupillary) and outer (limbic) circles of the iris and the detection of the upper and lower bound of the eyelids are performed. The inner circle is located on the iris and pupil boundary, the outer circle is located on the sclera and iris boundary. Today researchers follow different methods in finding pupillary and limbic

11 citations

Journal ArticleDOI
TL;DR: In this article, a spatio-temporal adaptation of the Siamese Neural Network is proposed, where one branch extracts spatial features using a 1D Convolutional Neural Network (CNN) while the other processes the input in the temporal domain using LSTMs.

11 citations

Proceedings ArticleDOI
03 Jun 1991
TL;DR: A planar shape-recognition approach is presented which is based on hidden Markov models and autoregressive parameters and explores the characteristic relations between consecutive segments to make classification at a finer level.
Abstract: A planar shape-recognition approach is presented which is based on hidden Markov models and autoregressive parameters. This approach segments closed shapes into segments and explores the characteristic relations between consecutive segments to make classification at a finer level. The algorithm can tolerate much shape contour perturbation, and a moderate amount of occlusion. The overall classification scheme is independent of shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained all over again when a new class of shapes is added. >

10 citations

Proceedings Article
01 Jan 1995
TL;DR: A method to automatically estimate the optimum ponderation of static and dynamic features in a speech recognition system based on Continuous-Density Hidden Markov Modelling (CDHMM), widely used in speech recognition.
Abstract: Speech dynamic feature are routinely used in current speech recognition systems in combination with short-term (static) spectral features. The aim of this paper is to propose a method to automatically estimate the optimum ponderation of static and dynamic features in a speech recognition system. The recognition system considered in this paper is based on Continuous-Density Hidden Markov Modelling (CDHMM), widely used in speech recognition. Our approach consists basically in 1) adding two new parameters for each state of each model that weight both kinds of speech features, and 2) estimating those parameters by means of a discriminative training algorithm that minimizes the recognition error using the recently proposed Generalized Probabilistic Descent (GPD) method. Experimental results in speaker independent digit recognition show an important increase of recognition accuracy.

10 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202219
202122
202028
201925
201832