A Review on Biometric Recognition

Kaur, Gagandeep; Singh, G.; Kumar, Vineet

doi:10.14257/ijbsbt.2014.6.4.07

Cited by 26 publications

(14 citation statements)

References 38 publications

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“…Multilayer Perceptron is a feed forward artificial neural network model with one or more layers between input and output layer in a directed graph [23]. The main advantage is that MLP is its stability and ease of use.…”

Section: Classificationmentioning

confidence: 99%

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

TuerxunWaili

Nor

Rahman

et al. 2016

Recent Advances in Information and Communication Technology 2016

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This paper presents a Multilayer Perception Neural Network developed to identify human subjects using electrocardiogram (ECG) signals. We use the amplitude values of Q, R and S as a features for our experiments. In this study, a total of 87 dataset were collected among 14 subjects from the Physikalisch-Technische Bundesanstalt (PTB) database. Out of the 14 subjects, Q-R-S feature points were taken from different day and time sessions to perform classification with MLP. Out of this data, 66 % is used as training dataset while the remaining 34 % is used for testing. Our method yields 96 % accuracy and demonstrates that the use of three fiducial points is sufficient to identify a subject despite the common practice of taking more feature points.

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Section: Classificationmentioning

confidence: 99%

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

TuerxunWaili

Nor

Rahman

et al. 2016

Recent Advances in Information and Communication Technology 2016

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“…While these can be lost, copied, or stolen, biometric systems are based on intrinsic traits that are always with the person and ensure the correspondence between the subject’s and the credential’s identities [2,3]. …”

Section: Introductionmentioning

confidence: 99%

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Pinto

Cardoso

Lourenço

et al. 2017

Sensors

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Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.

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“…PCAgives an s-dimensional vector representation of each face in a training set of images [9] . At the beginning of face recognition, the researchersonly adopted a small quality of features as represent the dataset.Only they had reconstructed the small number of features to form images, they contained the images resembled faces, which owned the name of Eigen images [10] .…”

Section: ) Face Recognition Biometricsmentioning

confidence: 99%

The Investigation on Multimodal Biometric Recognition

Geng¹,

Liu²

2015

Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

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Biometrics is used to identify individual in groups by their characteristics, such as fingerprint, face, iris, voice and behavioral characteristics like hand written signature and keystroke. Normally, a majority of biometrics recognition systems adopt methods by using unique feature to discriminate different individual from groups. But there is some forgery, subjective modifications like making up exiting as usual. A multimodal biometric recognition systemusing multiple biometrics to identify is proposed. In addition, it can support better Equal Error Rate (EER), False Acceptance Rate (FAR) and False Rejection Rate (FRR) by combining two or more physical or behavioral features. With lots of approached have been proposed, experts also pay much attention to the methods mentioned in recent years. The objective of this paper is to support a survey of multimodal biometric recognition system and approach using face, fingerprint recognition and enhanced iris characters that commonly used recently.

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A Review on Biometric Recognition

Abstract: Abstract

Cited by 26 publications

References 38 publications

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

The Investigation on Multimodal Biometric Recognition

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