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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14 Oct 1996
TL;DR: Simulation results show that the proposed approach for signature verification is rotation-invariant and scale- Invariant, and the preliminary results are very promising.
Abstract: Zernike moment invariants have been widely used in pattern recognition and image processing. In this paper, Chinese signature verification using normalized Zernike moment invariants (NZMI) is proposed. Different orders of moment invariants are studied. Simulation results show that the proposed approach for signature verification is rotation-invariant and scale-invariant, and the preliminary results we got are very promising. Furthermore experiments using projection are also carried out. The results are compared with those of NZMI.
11 citations
29 Sep 2013
TL;DR: This paper shows how an attacker can select a target with a similar biometric signature in order to increase their chances of false acceptance, and demonstrates this effect using a publicly available iris recognition algorithm.
Abstract: This paper argues that biometric verification evaluations can obscure vulnerabilities that increase the chances that an attacker could be falsely accepted. This can occur because existing evaluations implicitly assume that an imposter claiming a false identity would claim a random identity rather than consciously selecting a target to impersonate. This paper shows how an attacker can select a target with a similar biometric signature in order to increase their chances of false acceptance. It demonstrates this effect using a publicly available iris recognition algorithm. The evaluation shows that the system can be vulnerable to attackers targeting subjects who are enrolled with a smaller section of iris due to occlusion. The evaluation shows how the traditional DET curve analysis conceals this vulnerability. As a result, traditional analysis underestimates the importance of an existing score normalisation method for addressing occlusion. The paper concludes by evaluating how the targeted false acceptance rate increases with the number of available targets. Consistent with a previous investigation of targeted face verification performance, the experiment shows that the false acceptance rate can be modelled using the traditional FAR measure with an additional term that is proportional to the logarithm of the number of available targets.
11 citations
20 Dec 2008
TL;DR: A new face recognition algorithm based on gray-scale that shows higher recognition rate than eigenface algorithm in the same experiment conditions according to the practices.
Abstract: Face recognition has been a focus in research for the last couple of decades because of its wide potential applications and its importance to meet the security needs of today's world. However, the network is very complex and therefore it is difficult to train. To reduce complexity, neural network is often applied to the pattern recognition phase rather than to the feature extraction phase. In order to cope with such complication and find out the true invariant for recognition, researchers have developed various recognition algorithms. In this paper, we give a new face recognition algorithm based on gray-scale. The paper shows the readers the basic principle of this face recognition algorithm, the implement of the new face recognition approach, the advantage of the new algorithm, and shows higher recognition rate than eigenface algorithm in the same experiment conditions according to our practices.
11 citations
01 Nov 2016
TL;DR: A method for handwritten signature recognition based on fuzzy logic based on comparing these fuzzy features of handwritten signature based on curvature properties with fuzzy values which are better than some other methods.
Abstract: We suggested a method for handwritten signature recognition based on fuzzy logic. First of all, we proposed some features of handwritten signature based on curvature properties with fuzzy values. Then we proposed a method for signature recognition based on comparing these fuzzy features. We used collection of signatures MCYT_Signature_100 for testing our method. Signature recognition experiment has been conducted with 100 users, 25 original and 25 fake signatures for each user. As a result, we have got FRR value 0.03 and FAR value 0.01 which are better than the results of some other methods.
11 citations
14 Oct 2008
TL;DR: A novel approach of human activity recognition based on Linear Discriminant Analysis of Independent Component features from shape information is presented and Hidden Markov Model (HMM) is applied for training and recognition.
Abstract: In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.
11 citations