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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.


Papers
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Proceedings ArticleDOI
27 Jul 2016
TL;DR: The experimental results show that the proposed HARS offers high accuracy of action recognition in real-time.
Abstract: This paper aims at finding an efficient approach for automatic human action recognition to classify human actions in both outdoor and indoor environments. A human action recognition system (HARS) collects video frames of human activities, extracts the desired features of each human skeleton. These characteristics are calculated, classified to build a skeleton database that can distinguish almost human gestures. This HARS converts every sequence of human gestures to the sequences of skeletal joint mapping (SJM). Then it assigns corresponding observation symbols to each SJM. Those observation sequences are used to train of hidden Markov models (HMMs) corresponding to seven actions: standing, walking, running, jumping, falling, lying, and sitting. Baum-Welch and forward-backward algorithms are employed to find optimal parameters of each HMM. During a recognition phase, each human gesture sequence is converted to an observation sequence and put into seven optimized HMM models. The current action can be identified by finding a model with the highest probability. The experimental results show that the proposed HARS offers high accuracy of action recognition in real-time.

10 citations

Journal ArticleDOI
TL;DR: This paper proposes a new approach for biometric recognition using hand vein and finger vein images that employs hyper analytic wavelet transform and automatic thresholding techniques to extract features from the images of hand.
Abstract: Multi-modal biometric recognition uses more than one biometric identifier to recognise a person. Identification based on multiple templates becomes an emerging need. Multi-modal biometric systems are expected to be more reliable due to the presence of multiple independent pieces of biometric traits. This paper proposes a new approach for biometric recognition using hand vein and finger vein images. Our proposed method employs hyper analytic wavelet transform and automatic thresholding techniques to extract features from the images of hand. Once the veins are extracted the location and width are stored in the database. Here score level fusion based on fuzzy logic is utilised for recognition. This approach is tested on the finger vein and hand vein databases which is containing 500 samples from 100 users. The experimental results exhibits that the proposed method is comparable with the existing recognising methods.

10 citations

Journal ArticleDOI
TL;DR: One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system.
Abstract: A practically viable multi-biometric recognition system should not only be stable, robust and accurate but should also adhere to real-time processing speed and memory constraints. This study proposes a cascaded classifier-based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users?? dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance-based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier-based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.

10 citations

Journal ArticleDOI
TL;DR: A new and robust model for signature recognition by means of features inspired by the human's visual ventral stream is introduced which contains illumination and view invariant C2 features from all images in the dataset.
Abstract: 1 Abstract—This paper introduces a new and robust model for signature recognition by means of features inspired by the human's visual ventral stream. A feature set is extracted by means of a feed-forward model which contains illumination and view invariant C2 features from all images in the dataset. Also we use from Linear Discrimniant Analysis (LDA) to reduce the dimension of C2 feature vectors that is derived from a cortex-like mechanism. Then we utilized standard K-Nearest Neighbor (KNN) as classifier. The effectiveness of the approach is evaluated on an experimental signature database. By this new effort the rate of signature recognition is significantly high toward other models.

10 citations

25 Apr 2012
TL;DR: In this paper, a contour based technique is proposed for signature recognition, which is a simple and effective approach that can be easily implemented in a programming language and can be used for the recognition of the signature.
Abstract: In this paper we propose a technique that can be used for signature recognition. This technique is a contour based technique. Here we propose a simple and effective approach that can be easily implemented in a programming language.The paper deals with the recognition of the signature, as human operator generally makes the work of signature recognition. Hence the algorithm simulates human behavior, to achieve perfection and skill through AI. The logic that decides the extent of validity of the signature must implement Artificial Intelligence Pattern recognition is the science that concerns the description or classification of measurements, usually based on underlying model. Since most pattern recognition tasks are first done by humans and automated later, the most fruitful source of features has been to ask the people who classify the objects how they tell them a part. Signatures are a behavioral biometric that change over a period of time and are influenced by physical and emotional conditions of a subject. This technique gives acceptable results in a simple and fast way.

10 citations


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