HMM-based on-line signature verification: Feature extraction and signature modeling

Presents an approach to generating biometric hash values based on statistical features in online signature signals. Whilst the output of typical online signature verification systems are threshold-based true-false decisions, based on a comparison between test sample signals and sets of reference signals, our system responds to a signature input with a biometric hash vector, which is calculated based on an individual interval matrix. Especially for applications, which require key management strategies, hash values are of great interest, as keys can be derived directly from the hash value, whereas a verification decision can only grant or refuse access to a stored key. Further, our approach does not require storage of templates for reference signatures, thus increases the security of the system. In our prototype implementation, the generated biometric hash values are calculated on a pen-based PDA and used for key generation for a future secure data communication between a PDA and a server by encryption. First tests show that the system is actuality able to generate stable biometric hash values of the users and although the system was exposed to skilled forgeries, no test person was able to reproduce another subject's hash vector.

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Biometric hash based on statistical features of online signatures

An online biometric verification system for use over the Internet and requiring no specialist equipment is presented. Combining two distinct tests to ensure authenticity, a typing style test and a mouse-based signature test, achieves a fraudulent access rate of /spl ap/ 4.4 percent, while authentic users access with a rate of /spl ap/ 99 percent.

Java-based Internet biometric authentication system

Handwriting is one of the most common types of questioned writing encountered and frequently attracts the attention in litigation. Contrary to the physiological characteristics, handwriting is a behavioral characteristic thus no two individuals with mature handwriting are exactly alike or an individual cannot produce the others writing exactly. Writing behavior and individualities are examined for similarities for both specimen and questioned document, thus, it is very efficient and effective strategy for biometrics. In this paper, we present a comprehensive review of writer identification methods and intend to provide taxonomy of dataset, feature extraction methods, as well as classification (conventional and deep learning based) for writer identification. For ease of reader, we grouped the discussion into English, Arabic, Western and Other languages from script prospective, whereas, from algorithm and methods perspective, we grouped the discussion with respect to implementation steps sequence. In the end, we highlighted the challenges and open research issues in the field of writer identification. Finally, we also suggest future direction.

Writer identification using machine learning approaches: a comprehensive review

A new biometrics authentication method for cellular phones which has an advantage of simple and natural operation is proposed The proposed method authenticates owner using acceleration signals obtained by an acceleration sensor embedded in the cellular phones during his arm sweep action When the owner wants to unlock its security, he just needs to grasp and shake his cellular phone The proposed method verifies owner's acceleration signals by using a DP-matching algorithm which can adapt fluctuations caused by different grip Simulation results show that the proposed method can achieve the best equal error rate 50% for 22 testers

A Study on Biometric Authentication based on Arm Sweep Action with Acceleration Sensor

A signature is widely used to authorize who issued the document. However, a signature has ambiguity, and it is difficult to distinguish the authentic signature from the mimicked signature only by the bit-mapped pattern. It is expected that the altitude and the azimuth of the gripped pen under signing depends on the shape of the writer's hand and the habit of writing. We propose a new on-line writer authentication system using the pen altitude, pen azimuth, shape of signature, and writing pressure in real time. From experimental results with writing information by 24 writers, it is found that the authentication rate of 98% is obtained.

T. Hanamoto

Papers

On-line signature verification based on altitude and direction of pen movement