Signature recognition

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

eBiometrics: an enhanced multi-biometrics authentication technique for real-time remote applications on mobile devices

Abstract: The use of mobile communication devices with advance sensors is growing rapidly. These sensors are enabling functions such as Image capture, Location applications, and Biometric authentication such as Fingerprint verification and Face & Handwritten signature recognition. Such ubiquitous devices are essential tools in today's global economic activities enabling anywhere-anytime financial and business transactions. Cryptographic functions and biometric-based authentication can enhance the security and confidentiality of mobile transactions. Using Biometric template security techniques in real-time biometric-based authentication are key factors for successful identity verification solutions, but are venerable to determined attacks by both fraudulent software and hardware. The EU-funded SecurePhone project has designed and implemented a multimodal biometric user authentication system on a prototype mobile communication device. However, various implementations of this project have resulted in long verification times or reduced accuracy and/or security. This paper proposes to use built-in-self-test techniques to ensure no tampering has taken place on the verification process prior to performing the actual biometric authentication. These techniques utilises the user personal identification number as a seed to generate a unique signature. This signature is then used to test the integrity of the verification process. Also, this study proposes the use of a combination of biometric modalities to provide application specific authentication in a secure environment, thus achieving optimum security level with effective processing time. I.e. to ensure that the necessary authentication steps and algorithms running on the mobile device application processor can not be undermined or modified by an imposter to get unauthorized access to the secure system.

Face Recognition with Multi-stage Matching Algorithms

Abstract: of a Dissertation Submitted to the Graduate School of The University of Southern Mississippi in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

10-MHz 64-bit error-tolerant signature recognition circuit

Abstract: A circuit which identifies a 64-bit synchronization word in a serial incoming data stream is discussed. The signature is identified when the incoming data stream matches a previously known and stored signature. It is also possible to program the number of bit errors permissible for recognition of the signature. The circuit was fabricated in a 1.5- mu m CMOS technology for use in a D2MAC television decoder. The circuit operates at data rates up to 20 MHz and covers an area of 1.4*0.1 mm/sup 2/. The possible applications in image recognition and string identification in text are presented. >

Development of a VQ-HMM continuous speech speaker-independent recognition system for small vocabularies

Abstract: The authors describe the first phase in the development of a speech recognition system for small vocabularies. The system is designed to handle speaker-independent continuous speech and can be easily modified for different vocabularies. The system consists of a spectral analysis stage followed by vector quantization (VQ) and hidden Markov modeling (HMM). VQ is performed by multiple acoustic parameter codebooks, which are independent or dependent on speech units. The approach seeks to incorporate more knowledge about phoneme allophonic, linguistic, and speaker-dependent variations. The increase in number of codebooks is compensated by their decrease in size, minimizing effects on storage requirements. Phoneme HMM models permit an easy adaptation to new vocabulary requirements. A loop HMM structure with optional silences between words allows continuous speech recognition using a Viterbi search. Preliminary experiments on continuous phoneme and digit recognition were performed on an unrestricted-speaker telephone database. >

Multimodal biometric recognition using sclera and fingerprint based on ANFIS

Abstract: Biometrics is the ID of humans utilizing intrinsic physical, biological, otherwise activity features, traits, or habits. Biometrics has the potential to provide this desired ability to clearly and discretely determine a person's identity with additional accuracy and security. Biometric systems primarily based on individual antecedent of advice which is referred as unimodal frameworks. Even though some unimodal frameworks (e.g. Palm, Finger impression, Face, Iris), have got significant change in consistency plus precision yet has experienced selection issues attributable to non-all-inclusiveness of biometrics attributes, vulnerability to biometric mocking and insufficient exactness created by boisterous information as their inconveniences. In future, single biometric framework might not be in a position to accomplish the wanted execution prerequisite in genuine world provisions. To defeat these issues, we have to utilize multimodal biometric confirmation frameworks which blend data from various modalities to make a choice. Multimodal biometric confirmation framework utilize use more than one human modalities such as face, iris, retina, sclera and fingerprint etc. to improve their security of the method. In this approach, combined the biometric traits of sclera and fingerprint for addressing authentication issues, which has not discussed and implemented earlier. The fusion of multimodal biometric system helps to reduce the system error rates. The ANFIS model consolidated the neural system versatile capacities and the fluffy rationale qualitative strategy will have low false dismissal degree contrasted with neural network and fluffy rationale qualitative frame work. The combination of multimodal biometric security conspires in the ANFIS will show higher accuracy come close with Neural Network and Fuzzy Inference System.