PalmKeyNet: Palm Template Protection Based on Multi-modal Shared Key
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Modality (human–computer interaction)
Feature (linguistics)
Palm print
Single-modal biometrics there has been a lot of questions,which at accuracy,user acceptance,cost and other aspects of disadvantage are different.The use of multi-modal biometrics can improve the recognition performance,and adapt to their respective applications.This paper introduces the multi-modal biometrics and gives a multi-modal biometrics based on biometric in single-model.Finally based on the characteristics of human face and voice of the multi-modal biometrics biology-related experiments proves that multi-modal biometrics has significantly improved the identification of the previous single-modal certification at the level of performance and the user acceptance,etc.,and enhanced system robustness and practicality.
Robustness
Mode (computer interface)
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An identification and authentication device based on physical attributes like fingerprint, palm print, iris pattern, etc. is called biometric system. Many body parts, personal characteristics and imaging methods have been suggested and used for biometrics systems: fingers, hands, feet, faces, eyes, ears, teeth, veins, voices, signatures and gaits. Literature contains wide range of techniques occupying a large number of implemented algorithms regarding biometrics. In this paper we focus on iris recognition system in biometrics.
Iris Recognition
Hand geometry
Palm print
Identification
IRIS (biosensor)
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Multimodal biometrics provides high recognition accuracy and population coverage by combining different biometric sources. However, some multimodal biometrics may obtain smaller-than-expected improvement of recognition accuracy if the combined biometric sources are dependent in terms of a false acceptance by mistakenly perceiving biometric features from two different persons as being from the same person. In this paper, we propose our multimodal biometric prototype that captures a palm vein and three fingerprints simultaneously and we evaluate whether or not their combination is statistically independent. By evaluating false acceptance using the palm vein images and the fingerprint images collected with our prototype, we confirmed that the combination of the palm vein and the fingerprints is almost independent.
Palm print
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Modal expressions are expressions of a viewpoint and attitude. This study examines the nature and use of different modal categories. The main divisions are dynamic, deontic and apistemic modality. Within these domains of modality, special attention is given to the concepts of possibility and necessity and their different interpretations. From the resources of language, in the central position of the examination are the modal verbs. The study is both theoretical and empirical.
Modality (human–computer interaction)
Deontic logic
Position (finance)
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Biometrics are computerized methods of recognizing people based on physical or behavioral characteristics. The main biometric technologies include face recognition, fingerprint, hand geometry, iris, palm prints, signature and voice. Biometric technologies can work in two modes – authentication (one-to-one matching) and identification (one-to-many) matching. However, only three biometrics are capable of the latter – face, finger and iris.
Hand geometry
Identification
Iris Recognition
Palm print
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Palm print
Hand geometry
Feature (linguistics)
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Biometrics are automated methods of recognising a person based on physiological or behavioural characteristics. To discriminate individuals, multimodal biometrics has already proven as an effective strategy. Biometric features can be broadly classified as physiological features and behavioural features. Ear, face, and palm come under physiological features. Gait and signature verification come under behavioural features. Combining multiple human trait features for biometric identification is multimodal biometric identification. Here, ear and palm print are the two biometric modalities used for person identification fused at feature level. To extract the features for person identification, Multiblock Local Binary Pattern and Binarised Statistical Image Features are used. Required intrusive means for acquiring the information can be a common drawback when using biometric features such as iris pattern, facial traits, etc. To overcome the drawbacks, ear can be used as a biometric feature; it also has an advantage of no changes over time and not influenced by facial expressions.
Palm print
Identification
Feature (linguistics)
Modality (human–computer interaction)
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successful biometric system depends on the accuracy with which it works. The degree of accuracy is measured with biometric parameters. This paper addresses the various parameters used in the analysis and measurement of a Biometric system. This paper also presents experimental results of a palm biometric with a POLY U database. The performance is measured with a variable threshold.
Palm print
Biometric data
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Biometric is a measuring technique that is utilised to discern a person's identity through a physiological or behavioural feature. Each modality on its own cannot always be reliable for recognition. The multimodal biometric system offers several advantages over the traditional biometric system. In order to overcome the limitations of the unimodal biometrics, the fusion of multimodal biometric traits have been used. In this paper, a multimodal biometric based on fingerprint, palm print and knuckle print is presented. Gabor filter is employed to extract features from these biometric traits. The features extracted from these biometric traits are normalised and fused together. The features are fused by feature level fusion, and the result of the proposed method provides efficient authentication, by minimising the FAR by 1.43%, and the framework achieves satisfactory performance.
Palm print
Feature (linguistics)
Gabor filter
Modality (human–computer interaction)
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Citations (2)