Signature recognition

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

Biometric information processing apparatus, biometric information processing method, and biometric information processing computer program

Abstract: A biometric information processing apparatus includes: a biometric information acquiring unit which acquires a user's biometric information and generates a biometric input image representing the biometric information; and a processing unit. The processing unit implements: generating a first intermediate image by applying first image processing to the biometric input image; generating a second intermediate image by applying second image processing to the biometric input image; detecting, from each of the first and second intermediate images, a singular point candidate; calculating a distance between the singular point candidates detected from each of the first and second intermediate images for the same singular point contained in the biometric information; calculating a quality metric for the biometric input image based on the distance; and if the quality metric is not higher than a predefined threshold value, then prompting the user to have the user's biometric information reacquired by the biometric information acquiring unit.

Biometric template protection for speaker recognition based on universal background models

Abstract: (Voice-) biometric data is considered as personally identifiable information, that is, the increasing demand on (mobile) speaker recognition systems calls for applications which prevent from privacy threats, such as identity-theft or tracking without consent. Technologies of biometric template protection, in particular biometric cryptosystems, fulfil standardised properties of irreversibility and unlinkability which represent appropriate countermeasures to such vulnerabilities of conventional biometric recognition systems. Thereby, public confidence in and social acceptance of biometric applications is strengthened. In this work the authors propose a binarisation technique, which is used to extract scalable high-entropy binary voice reference data (templates) from speaker models, based on Gaussian mixture models and universal background models. Binary feature vectors are then protected within a template protection scheme in particular, fuzzy commitment scheme, in which error correction list-decoding is employed to overcome high intra-class variance of voice samples. In experiments, which are evaluated out on a text-independent speaker corpus of 339 individuals, it is demonstrated that the fully ISO/IEC IS 24745 compliant system achieves privacy protection at a negligible loss of biometric performance, confirming the soundness of the presented approach.

The analysis of error in an on-line recognition system of Arabic handwritten characters

Abstract: In this paper we describe a system that recognizes on-line Arabic handwriting characters. In this system, a dynamic programming algorithm is implemented. We present here the effects of some parameters of the system on its performances and on its recognition errors. This study shows a way to select the optimum values for some key parameters of the system to obtain minimum recognition error rates.

Multimodal biometric recognition using iris feature extraction and palmprint features

Abstract: A Biometric system is essentially a pattern recognition system that makes use of biometric traits to recognize individuals. Authentication systems built on only one biometric modality may not fulfill the requirements of demanding applications in terms of properties such as performance, acceptability and distinctiveness. Most of the unimodal biometrics systems have problems such as noise in collected data, intra-class variations, inter-class variations, non universality etc. Some of these limitations can be overcome by multiple source of information for establishing identity; such systems are known as multimodal biometric systems. In this paper a multi modal biometric system of iris and palm print based on Wavelet Packet Analysis is described. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. Palm is the inner surface of a hand between the wrist and the fingers. Palmprint is referred to principal lines, wrinkles and ridges on the palm. The visible texture of a person's iris and palm print is encoded into a compact sequence of 2-D wavelet packet coefficients, which generate a “feature vector code”. In this paper, we propose a novel multi-resolution approach based on Wavelet Packet Transform (WPT) for texture analysis and recognition of iris and palmprint. The development of this approach is motivated by the observation that dominant frequencies of iris texture are located in the low and middle frequency channels. With an adaptive threshold, WPT sub images coefficients are quantized into 1, 0 or −1 as iris signature. This signature presents the local information of different irises. By using wavelet packets the size of the biometric signature of code attained is 960 bits. The signature of the new pattern is compared against the stored pattern after computing the signature of new input pattern. Identification is performed by computing the hamming distance.