MULTIMODAL BIOMETRICS: MOST APPROPRIATE FOR PERSON IDENTIFICATION

2017 ◽  
Vol 4 (3) ◽  
pp. 1 ◽  
Author(s):  
BARDE SNEHLATA ◽  
Keyword(s):  
Multimodal Biometrics ◽  
Person Identification

Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks

2021 ◽  
Author(s):  
M. S. Lohith ◽  
Yoga Suhas Kuruba Manjunath ◽  
M. N. Eshwarappa
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Feature Representation ◽  
Biometric System ◽  
Multimodal Biometrics ◽  
Person Identification ◽  
Large Dataset ◽  
Remarkable Improvement ◽  
Age Variations ◽  
Periocular Region

Biometrics is an active area of research because of the increase in need for accurate person identification in numerous applications ranging from entertainment to security. Unimodal and multimodal are the well-known biometric methods. Unimodal biometrics uses one biometric modality of a person for person identification. The performance of an unimodal biometric system is degraded due to certain limitations such as: intra-class variations and nonuniversality. The person identification using more than one biometric modality of a person is multimodal biometrics. This method of identification has gained more interest due to resistance on spoof attacks and more recognition rate. Conventional methods of feature extraction have difficulty in engineering features that are liable to more variations such as illumination, pose and age variations. Feature extraction using convolution neural network (CNN) can overcome these difficulties because large dataset with robust variations can be used for training, where CNN can learn these variations. In this paper, we propose multimodal biometrics at feature level horizontal fusion using face, ear and periocular region biometric modalities and apply deep learning CNN for feature representation and also we propose face, ear and periocular region dataset that are robust to intra-class variations. The evaluation of the system is made by using proposed database. Accuracy, Precision, Recall and [Formula: see text] score are calculated to evaluate the performance of the system and had shown remarkable improvement over existing biometric system.

Human Ear Pattern Recognition System

2017 ◽  
Vol 7 (8) ◽  
pp. 117 ◽  
Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar
Keyword(s):  
Pattern Recognition ◽  
Expectation Maximization Algorithm ◽  
Recognition System ◽  
Modern World ◽  
Person Identification ◽  
Biometric Systems ◽  
Different Types ◽  
Recognition Systems ◽  
Human Ear ◽  
Pca Algorithm

In the modern world, human recognition systems play an important role to   improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm.  Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

Sign in / Sign up

Export Citation Format

Share Document