scholarly journals Multimodal Biometric Recognition Based on 3D Ultrasound Palmprint-Hand Geometry Fusion

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Antonio Iula ◽  
Monica Micucci
Keyword(s):  
3D Ultrasound ◽  
Biometric Recognition ◽  
Hand Geometry ◽  
Multimodal Biometric Recognition

A Multimodal Biometric Recognition of Touched Fingerprint and Finger-Vein

2011 ◽  
Author(s):  
Young Ho Park ◽  
Dat Nguyen Tien ◽  
Hyeon Chang Lee ◽  
Kang Ryoung Park ◽  
Eui Chul Lee ◽  
...  
Keyword(s):  
Biometric Recognition ◽  
Finger Vein ◽  
Multimodal Biometric Recognition

scholarly journals A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4784-4796
Author(s):  
Ibrahim Omara ◽  
Ahmed Hagag ◽  
Souleyman Chaib ◽  
Guangzhi Ma ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Support Vector ◽  
Distance Metric ◽  
Biometric Recognition ◽  
Model Combining ◽  
Multimodal Biometric Recognition

HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition

Sensor Review ◽  
2020 ◽  
Vol 40 (2) ◽  
pp. 203-216
Author(s):  
S. Veluchamy ◽  
L.R. Karlmarx
Keyword(s):  
Research Field ◽  
Biometric Identification ◽  
Svm Classifier ◽  
Identification System ◽  
Biometric Recognition ◽  
Data Set ◽  
Content Type ◽  
Proposed Model ◽  
Simulation Results ◽  
Multimodal Biometric Recognition

Purpose Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features. Design/methodology/approach The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector. Findings Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1. Originality/value In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

GENETIC ALGORITHM BASED FEATURE SELECTION LEVEL FUSION USING FINGERPRINT AND IRIS BIOMETRICS

2008 ◽  
Vol 22 (03) ◽  
pp. 585-600 ◽  
Author(s):  
A. ALPASLAN ALTUN ◽  
H. ERDINC KOCER ◽  
NOVRUZ ALLAHVERDI
Keyword(s):  
Feature Selection ◽  
Degrees Of Freedom ◽  
Recognition System ◽  
Test Results ◽  
Biometric Recognition ◽  
Training Time ◽  
Iris Biometrics ◽  
Multimodal Biometric Recognition ◽  
Selection Of ◽  
Combined Data

An accuracy level of unimodal biometric recognition system is not very high because of noisy data, limited degrees of freedom, spoof attacks etc. problems. A multimodal biometric system which uses two or more biometric traits of an individual can overcome such problems. We propose a multimodal biometric recognition system that fuses the fingerprint and iris features at the feature extraction level. A feed-forward artificial neural networks (ANNs) model is used for recognition of a person. There is a need to make the training time shorter, so the feature selection level should be performed. A genetic algorithms (GAs) approach is used for feature selection of a combined data. As an experiment, the database of 60 users, 10 fingerprint images and 10 iris images taken from each person, is used. The test results are presented in the last stage of this research.

Feature fusion of palmprint and face via tensor analysis and curvelet transform

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
X. Xu ◽  
X. Guan ◽  
D. Zhang ◽  
X. Zhang ◽  
W. Deng ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Curvelet Transform ◽  
Image Features ◽  
Tensor Analysis ◽  
Small Samples ◽  
Palmprint Recognition ◽  
Biometric Recognition ◽  
Fusion Level ◽  
Knn Classifier ◽  
Multimodal Biometric Recognition

AbstractIn order to improve the recognition accuracy of the unimodal biometric system and to address the problem of the small samples recognition, a multimodal biometric recognition approach based on feature fusion level and curve tensor is proposed in this paper. The curve tensor approach is an extension of the tensor analysis method based on curvelet coefficients space. We use two kinds of biometrics: palmprint recognition and face recognition. All image features are extracted by using the curve tensor algorithm and then the normalized features are combined at the feature fusion level by using several fusion strategies. The k-nearest neighbour (KNN) classifier is used to determine the final biometric classification. The experimental results demonstrate that the proposed approach outperforms the unimodal solution and the proposed nearly Gaussian fusion (NGF) strategy has a better performance than other fusion rules.

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