8 research outputs found
Bimodal Biometric Verification Mechanism using fingerprint and face images(BBVMFF)
An increased demand of biometric authentication coupled with automation of systems is observed in the recent times. Generally biometric recognition systems currently used consider only a single biometric characteristic for verification or authentication. Researchers have proved the inefficiencies in unimodal biometric systems and propagated the adoption of multimodal biometric systems for verification. This paper introduces Bi-modal Biometric Verification Mechanism using Fingerprint and Face (BBVMFF). The BBVMFF considers the frontal face and fingerprint biometric characteristics of users for verification. The BBVMFF Considers both the Gabor phase and magnitude features as biometric trait definitions and simple lightweight feature level fusion algorithm. The fusion algorithm proposed enables the applicability of the proposed BBVMFF in unimodal and Bi-modal modes proved by the experimental results presented
LDA-PAFF: Linear Discriminate Analysis Based Personal Authentication using Finger Vein and Face Images
Biometric based identifications are widely used for individuals personnel identification in recognition system. The unimodal recognition systems currently suffer from noisy data, spoofing attacks, biometric sensor data quality and many more. Robust personnel recognition can be achieved considering multimodal biometric traits. In this paper the LDA (Linear Discriminate analysis) based Personnel Authentication using Finger vein and Face Images (LDA-PAFF) is introduced considering the Finger Vein and Face biometric traits. The Magnitude and Phase features obtained from Gabor Kernels is considered to define the biometric traits of personnel. The biometric feature space is reduced using Fischer Score and Linear Discriminate Analysis. Personnel recognition is achieved using the weighted K-nearest neighbor classifier. The experimental study presented in the paper considers the (Group of Machine Learning and Applications, Shandong University-Homologous Multimodal Traits) SDUMLA-HMT multimodal biometric dataset. The performance of the LDA-PAFF is compared with the existing recognition systems and the performance improvement is proved through the results obtained
Multimodal biometric authentication using ECG and fingerprint
Biometric system is a very important recognition system
which is used for individual verification and identification.
Various types of biometric traits are used in today's world, in
which some are used for commercial purpose and few used
for verification purpose. Existing authentication techniques
are suffer from different errors like mismatch image,
spoofing, falsification in the data, to solve this errors the
combination of Electrocardiography(ECG) and fingerprint
multimodal is introduced. This proposed modal produces
effective recognition system when compared to individual
recognition system. The proposed multimodal recognition
system provides optimum results compared to the individual
recognition system which yields better results for
authentication compared to the Existing system
Multi model Personal Authentication using Finger vein and Face Images (MPAFFI)
Biometric based identifications are widely adopted for personnel identification. The unimodal recognition systems currently suffer from noisy data, spoofing attacks, biometric sensor data quality and many more. Robust personnel recognition considering multimodal biometric traits can be achieved. This paper introduces the Multimodal Personnel Authentication using Finger vein and Face Images (MPAFFI) considering the Finger Vein and Face biometric traits. The use of Magnitude and Phase features obtained from Gabor Kernels is considered to define the biometric traits of personnel. The biometric feature space is reduced using Fischer Score and Linear Discriminate Analysis. Personnel recognition is achieved using the weighted K-nearest neighbor classifier. The experimental study presented in the paper considers the (Group of Machine Learning and Applications, Shandong University-Homologous Multimodal Traits) SDUMLA - HMT multimodal biometric dataset. The performance of the MPAFFI is compared with the existing recognition systems and the performance improvement is proved through the results obtained. © 2014 IEEE
Efficient iris retrieval using neural networks
Biometrics in high technology refers to the automatic recognition of individuals physiological and behavioral traits which determines their identity. Iris recognition is one of the most reliable and widely used biometric techniques for personal identification and security. The major objective of the work presented here is to accurately recognize the users with iris patterns and with faster computation results. In this paper, we have proposed an architecture that makes use of sobel edge detection, discrete wavelet transform (DWT), four neighbours concept (FNC) for processing the images that achieve higher efficiency. The proposed approach utilizes neural networks for accurate and computationally efficient recognition
Multimodel Biometrics Using ECG and Fingerprint
system is a very important recognition system which is used for individual verification and identification. Various types of biometric traits are used in today's world, in which some are used for commercial purpose and few used for verification purpose. Existing authentication techniques are suffer from different errors like mismatch image, spoofing, falsification in the data, to solve this errors the combination of Electrocardiography(ECG) and fingerprint multimodal is introduced. This proposed modal produces effective recognition system when compared to individual recognition system. The proposed multimodal recognition system provides optimum results compared to the individual recognition system which yields better results for authentication compared to the Existing system. Keywordsm (ECG), Fingerprint, Authentication, Multimodal
Multimodel Biometrics Using ECG and Fingerprint
system is a very important recognition system which is used for individual verification and identification. Various types of biometric traits are used in today's world, in which some are used for commercial purpose and few used for verification purpose. Existing authentication techniques are suffer from different errors like mismatch image, spoofing, falsification in the data, to solve this errors the combination of Electrocardiography(ECG) and fingerprint multimodal is introduced. This proposed modal produces effective recognition system when compared to individual recognition system. The proposed multimodal recognition system provides optimum results compared to the individual recognition system which yields better results for authentication compared to the Existing system. Keywordsm (ECG), Fingerprint, Authentication, Multimoda