Topic
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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01 Aug 2006
TL;DR: A signature recognition method based on the fuzzy logic and genetic algorithm (GA) methodologies that achieved a signature recognition rate of about 90% and handled the random forgeries with 77 % accuracy and skilled forgery with 70% accuracy.
Abstract: This paper proposed a signature recognition method based on the fuzzy logic and genetic algorithm (GA) methodologies. It consists of two phases; the fuzzy inference system training using GA and the signature recognition. A sample of signatures is used to represent a particular person. The feature extraction process is followed by a selective preprocessing. The fuzzy inference system is followed by a feature extraction step. The projection profiles, contour profiles, geometric centre, actual dimensions, signature area, local features, and the baseline shift are considered as the feature set in the study. The input feature set is divided into five sections and separate five fuzzy subsystems were used to take the results. Those results are combined using a second stage fuzzy system. The fuzzy membership functions are optimized using the GA. A set of signatures consisting of genuine signatures, random forgeries, skilled forgeries of a particular signature and different signatures were used as the training set. Then, that particular optimized recognition system can be used to identify the particular signature identity. System achieved a signature recognition rate of about 90% and handled the random forgeries with 77 % accuracy and skilled forgeries with 70% accuracy. The recognition results authenticate that this is a reliable and accurate system for off-line recognition of handwritten signatures
15 citations
26 Oct 2004
TL;DR: This paper focuses on handling the two-dimensional feature of on-line handwriting signals in recognition engines, and compares formally and experimentally a number of solutions on various character recognition tasks.
Abstract: This paper focuses on handling the two-dimensional feature of on-line handwriting signals in recognition engines. This spatial information is taken into account in various ways depending on the nature of characters to be recognized. We review some techniques used in the literature and investigate new ones to represent and model the spatial information in handwriting recognition engines. We compare formally and experimentally a number of solutions on various character recognition tasks.
15 citations
TL;DR: A new method for face recognition using dynamic 3D range sequences is proposed and the performance is compared with that of conventional face recognition algorithms based on descriptors.
Abstract: 3D face recognition has attracted attention in the last decade due to improvement of technology of 3D image acquisition
and its wide range of applications such as access control, surveillance, human-computer interaction and biometric
identification systems. Most research on 3D face recognition has focused on analysis of 3D still data. In this work, a new
method for face recognition using dynamic 3D range sequences is proposed. Experimental results are presented and
discussed using 3D sequences in the presence of pose variation. The performance of the proposed method is compared
with that of conventional face recognition algorithms based on descriptors.
15 citations
18 Aug 1997
TL;DR: This paper presents two approaches for handling rejects in a hidden Markov based handwriting recognition system, one of the techniques relies on relative frequencies of those values, the other one utilizes standard classification techniques to train a reject decision unit, the reject classifier.
Abstract: The most scientific papers dealing with handwriting recognition systems make statements relating to the recognition performance based on a forced-recognition rate. This rate describes the ratio between the number of the correct recognized samples and the number of all possible samples. For systems applied in real applications this rate is not very relevant. They have to work with a very low error-rate, which can be only achieved by introducing effective reject criteria. So the real interesting thing is a function describing the recognition rate in relation to a specific error rate, including implicitly a corresponding reject rate. This paper presents two approaches for handling rejects in a hidden Markov based handwriting recognition system. The features to determine a reject are values which are derived from the hidden Markov recognizer. One of the techniques relies on relative frequencies of those values, the other one utilizes standard classification techniques to train a reject decision unit, the reject classifier. Both methods are presented with some noteworthy results.
15 citations
TL;DR: A one- dimensional feature set is introduced, which embeds two-dimensional information into an observation sequence of one-dimensional string, selected from a code-book, which provides a consistent normalization among distinct classes of shapes, which is very convenient for Hidden Markov Model (HMM) based shape recognition schemes.
15 citations