Signature recognition

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

Face Recognition Based on Features Measurement Technique

Abstract: We present a new technique to infer dimensions that can be used in biometric face recognition. The methodology is centered on inferring unique dimensions from human ears which provides unique physical biometric features. The process of determining the distance is done by harvesting the real actual dimensions from 2D faces images. This is achieved by using specific point to point distances on the two ears in human face. The points chosen give dimension information which enables discrimination for face recognition. The empirical results confirm that an accuracy of 94% recognition rate is achievable. The different positions of measurement points on the ears have a powerful impact to reduce the error of face recognition. Hence, our new measurement dimensions technique is precise and nodal facial points can be reflected as a robust face recognition method.

Persian off-line signature recognition with structural and rotation invariant features using by one-against-all svm classifier

Abstract: The problem of automatic signature recognition has received little attention in comparison with the problem of signature verification, despite its potential applications for many business processes and can be used effectively in paperless office projects. This paper presents model-based off-line signature recognition with rotation invariant features. Non-linear rotation of signature patterns is one of the major difficulties to be solved in this problem. The proposed system is designed based on support vector machines (SVM) classifier technique and rotation invariant structure feature to tackle the problem. Our designed system consists of three stages: the first is preprocessing stage, the second is feature extraction stage and the last is SVM classifier stage. Experimental results demonstrated that the proposed methods were effective to improve recognition accuracy.

Learning the hash code with generalised regression neural networks for handwritten signature biometric data retrieval

Abstract: Handwritten signature recognition is one important component of biometric authentication This is a central process in a broad range of areas requiring personal identification, such as security, legal contracts and bank transactions Extensive efforts have been put into the research towards the verification of handwritten signatures, which contain biometric information Although many successful methods have been used, they often disregard the size of databases, which can be very large, posing scalability problems to their application in real-world scenarios To overcome this problem, in this paper, we use binary embeddings of high-dimensional data which is an efficient tool for indexing big datasets of biometric images The rationale is to find a good hash function such that similar data points in Euclidean space preserve their similarities in the resulting Hamming space for fast data retrieval and state-of-the-art classification performance In the settings of an handwritten signature retrieval system, an indexing hashing-based scheme is presented We propose to learn k-bits hash code with a generalised regression neural network (GRNN), which yielded competitive results in the GPDS database

Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris Images

Abstract: .............................................................................................................................. 2 Acknowledgments............................................................................................................... 4 1 Chapter 1................................................................................................................... 14 Introduction....................................................................................................................... 14 1.1 Thesis Objectives .............................................................................................. 14 1.2 Thesis contributions .......................................................................................... 16 1.3 Thesis outline .................................................................................................... 19 1.4 Image databases ................................................................................................ 21 2 Chapter 2................................................................................................................... 23 Biometrics review ............................................................................................................. 23 2.1 Biometrics technology ...................................................................................... 23 2.2 Multimodal biometric systems.......................................................................... 28 2.3 Properties of Biometrics ................................................................................... 29 2.4 Classification of Biometric System .................................................................. 31 2.5 Biometric Sample Quality Measures ................................................................ 34 2.6 Face recognition................................................................................................ 35 2.7 Face tracking algorithms................................................................................... 39 2.7.1 Knowledge-Based Classifiers ................................................................... 40 2.7.2 Learning-Based Classifiers ....................................................................... 45 2.7.3 Motion estimation ..................................................................................... 47 2.8 Iris Recognition................................................................................................. 49 2.8.1 Iris structure .............................................................................................. 52 2.8.2 Iris texture pattern and colors ................................................................... 52 2.8.3 Imaging Systems....................................................................................... 55 2.8.4 Iris Localization and Segmentation .......................................................... 57 2.8.5 Size-invariant Unwrapping and Representation ....................................... 64 2.8.6 Feature Extraction..................................................................................... 67 2.8.7 Matching Algorithms and Distance Measure ........................................... 73 2.8.8 Evaluation Metrics .................................................................................... 75 2.8.9 Image Database and Open Source Software............................................. 80 2.9 Non-Cooperative Iris Recognition.................................................................... 81 2.10 Summary........................................................................................................... 84 3 Chapter 3................................................................................................................... 86 Using infrared illumination to improve eye & face tracking in low quality video images ........................................................................................................................................... 86 3.