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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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01 Jan 2009
TL;DR: This paper proposes a fusion of two different feature sets, one extracted from the morphological features and the other from statistical features, of the same biometric template, namely the hand vein biometric, which gives the accuracy of a multimodal system at the speed and cost of a unimodal systems.
Abstract: The major concern in a Biometric Identification System is its accuracy. In spite of the improvements in image acquisition and image processing techniques, the amount of research still being carried out in person verification and identification show that a recognition system which gives 0% FAR (False Acceptance Rate) and FRR (False Rejection Rate) is still not a reality. Multibiometric systems which combine two different biometric modalities or two different representations of the same biometric, to verify a person’s identity are a means of improving the accuracy of a biometric system. The former case however requires the user to produce his biometric identity two times to two different sensors. The image processing and pattern matching activities also increase nearly twofold compared to unimodal systems. In this paper we propose a fusion of two different feature sets, one extracted from the morphological features and the other from statistical features, of the same biometric template, namely the hand vein biometric. The proposed system gives the accuracy of a multimodal system at the speed and cost of a unimodal system.

42 citations

Proceedings ArticleDOI
01 Aug 1999
TL;DR: A face recognition system based on 2-D DCT features and pseudo-2D Hidden Markov Models is presented that achieves a recognition rate of 99.5% on the Olivetti Research Laboratory (ORL) face database, much better than a previous pseudo 2D HMM approach.
Abstract: A face recognition system based on 2-D DCT features and pseudo-2D Hidden Markov Models is presented. The system achieves a recognition rate of 99.5% on the Olivetti Research Laboratory (ORL) face database. This recognition rate is much better than the recognition rate of a previous pseudo 2-D HMM approach. Only one single face out of the 200 available test faces was not correctly recognized. The superiority of our approach against the previous approach is analyzed, and the recognition rates are compared to other face recognition systems evaluated on the ORL database.

41 citations

Journal ArticleDOI
TL;DR: The proposed system outperforms the winner of SVC with a reduced computational requirement, which is around 47 times lower than DTW, and is more privacy-friendly as it is not possible to recover the original signature using the codebooks.

41 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: The LPSNet is proposed, an end-to-end deep neural network based hand gesture recognition framework with novel log path signature features and a new method based on PS and LPS to effectively combine RGB and depth videos.
Abstract: Hand gesture recognition is gaining more attentions because it's a natural and intuitive mode of human computer interaction. Hand gesture recognition still faces great challenges for the real-world applications due to the gesture variance and individual difference. In this paper, we propose the LPSNet, an end-to-end deep neural network based hand gesture recognition framework with novel log path signature features. We pioneer a robust feature, path signature (PS) and its compressed version, log path signature (LPS) to extract effective feature of hand gestures. Also, we present a new method based on PS and LPS to effectively combine RGB and depth videos. Further, we propose a statistical method, DropFrame, to enlarge the data set and increase its diversity. By testing on a well-known public dataset, Sheffield Kinect Gesture (SKIG), our method achieves classification rate as 96.7% (only use RGB videos) and 98.7% (combining RGB and Depth videos), which is the best result comparing with state-of-the-art methods.

41 citations

Journal ArticleDOI
TL;DR: This paper presents a recognition system for offline signatures using Discrete Cosine Transform (DCT) and Hidden Markov Model (HMM) and shows that successful signatures recognition rates of 99.2% is possible.
Abstract: HMM has been used successfully to model speech and online signature in the past two decades. The success has been attributed to the fact that these biometric traits have time reference. Only few HMM based offline signature recognition systems have be developed because offline signature lack time reference. This paper presents a recognition system for offline signatures using Discrete Cosine Transform (DCT) and Hidden Markov Model (HMM). The signature to be trained or recognized is vertically divided into segments at the centre of gravity using the space reference positions of the pixels. The number of segmented signature blocks is equal to the number of states in the HMM for each user notwithstanding the length of the signatures. Experimental result shows that successful signatures recognition rates of 99.2% is possible. The result is better in comparison with previous related systems based on HMM and statistical classifiers.

41 citations


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