A Hybrid Approach for Person Identification Using Palmprint and Face Biometrics

2015 ◽  
Vol 29 (06) ◽  
pp. 1556009 ◽  
Author(s):  
Mina Farmanbar ◽  
Önsen Toygar
Keyword(s):  
Hybrid Approach ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Extraction Methods ◽  
Score Level Fusion ◽  
Linear Discriminant ◽  
Hybrid Approaches ◽  
Fusion Methods ◽  
Level Fusion

This paper proposes hybrid approaches based on both feature level and score level fusion strategies to provide a robust recognition system against the distortions of individual modalities. In order to compare the proposed schemes, a virtual multimodal database is formed from FERET face and PolyU palmprint databases. The proposed hybrid systems concatenate features extracted by local and global feature extraction methods such as Local Binary Patterns, Log Gabor, Principal Component Analysis and Linear Discriminant Analysis. Match score level fusion is performed in order to show the effectiveness and accuracy of the proposed schemes. The experimental results based on these databases reported a significant improvement of the proposed schemes compared with unimodal systems and other multimodal face–palmprint fusion methods.

A NEW APPROACH FOR FACE-IRIS MULTIMODAL BIOMETRIC RECOGNITION USING SCORE FUSION

2013 ◽  
Vol 27 (03) ◽  
pp. 1356004 ◽  
Author(s):  
MARYAM ESKANDARI ◽  
ÖNSEN TOYGAR ◽  
HASAN DEMIREL
Keyword(s):  
Recognition Performance ◽  
Recognition System ◽  
Extraction Methods ◽  
Score Level Fusion ◽  
New Approach ◽  
Fusion Methods ◽  
Score Fusion ◽  
Multimodal Biometric Recognition ◽  
Matching Score ◽  
Level Fusion

In this paper, a new approach based on score level fusion is presented to obtain a robust recognition system by concatenating face and iris scores of several standard classifiers. The proposed method concatenates face and iris match scores instead of concatenating features as in feature-level fusion. The features from face and iris are extracted using local and global feature extraction methods such as PCA, subspace LDA, spPCA, mPCA and LBP. Transformation-based score fusion and classifier-based score fusion are then involved in the process to obtain, concatenate and classify the matching scores. Different fusion techniques at matching score level, feature level and decision level are compared with the proposed method to emphasize improvement and effectiveness of the proposed method. In order to validate the proposed scheme, a combined database is formed using ORL and BANCA face databases together with CASIA and UBIRIS iris databases. The results based on recognition performance and ROC analysis demonstrate that the proposed score level fusion achieves a significant improvement over unimodal methods and other multimodal face-iris fusion methods.

PCANet based biometric system with fusion of palmprint and dorsal hand vein

2021 ◽  
pp. 1-9
Author(s):  
Surinder kaur ◽  
Gopal Chaudhary ◽  
Javalkar Dinesh kumar
Keyword(s):  
Recognition System ◽  
Personal Identification ◽  
Identification System ◽  
Biometric System ◽  
Score Level Fusion ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Fusion Methods ◽  
Dorsal Hand Vein ◽  
Level Fusion

Nowadays, Biometric systems are prevalent for personal recognition. But due to pandemic COVID 19, it is difficult to pursue a touch-based biometric system. To encourage a touchless biometric system, a less constrained multimodal personal identification system using palmprint and dorsal hand vein is presented. Hand based Touchless recognition system gives a higher user-friendly system and avoids the spread of coronavirus. A method using Convolution Neural Networks(CNN) to extract discriminative features from the data samples is proposed. A pre-trained function PCANeT is used in the experiments to show the performance of the system in fusion scheme. This method doesn’t require keeping the palm in a specific position or at a certain distance like most other papers. Different patches of ROI are used at two different layers of CNN. Fusion of palmprint and dorsal hand vein is done for final result matching. Both Feature level and score level fusion methods are compared. Results shows the accuracy of upto 98.55% and 98.86% and Equal error rate (EER) of upto 1.22% and 0.93% for score level fusion and feature level fusion, respectively. Our method gives higher accurate results in a less constrained environment.

Ear Recognition Based on Fusion of Ear and Tragus Under Different Challenges

2018 ◽  
Vol 32 (09) ◽  
pp. 1856009 ◽  
Author(s):  
Esraa Alqaralleh ◽  
Önsen Toygar
Keyword(s):  
Recognition Performance ◽  
Local Binary Patterns ◽  
Partial Occlusion ◽  
Score Level Fusion ◽  
Ear Recognition ◽  
Fusion Methods ◽  
Pose Variation ◽  
Level Fusion ◽  
Biometric Trait ◽  
Better Than

This paper proposes a 2D ear recognition approach that is based on the fusion of ear and tragus using score-level fusion strategy. An attempt to overcome the effect of partial occlusion, pose variation and weak illumination challenges is done since the accuracy of ear recognition may be reduced if one or more of these challenges are available. In this study, the effect of the aforementioned challenges is estimated separately, and many samples of ear that are affected by two different challenges concurrently are also considered. The tragus is used as a biometric trait because it is often free from occlusion; it also provides discriminative features even in different poses and illuminations. The features are extracted using local binary patterns and the evaluation has been done on three datasets of USTB database. It has been observed that the fusion of ear and tragus can improve the recognition performance compared to the unimodal systems. Experimental results show that the proposed method enhances the recognition rates by fusion of parts that are nonoccluded with tragus in the cases of partial occlusion, pose variation and weak illumination. It is observed that the proposed method performs better than feature-level fusion methods and most of the state-of-the-art ear recognition systems.

Multimodal score level fusion for recognition using face and palmprint

2020 ◽  
pp. 002072092092966
Author(s):  
Milind E Rane ◽  
Umesh S Bhadade
Keyword(s):  
Recognition System ◽  
Acceptance Rate ◽  
Face Database ◽  
Biometric Recognition ◽  
False Acceptance Rate ◽  
Score Level Fusion ◽  
Score Fusion ◽  
Matching Score ◽  
Level Fusion ◽  
Fusion Approach

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

A Lyapunov Theory-Based Neural Network Approach for Face Recognition

2010 ◽  
pp. 23-48 ◽  
Author(s):  
Li-Minn Ang ◽  
King Hann Lim ◽  
Kah Phooi Seng ◽  
Siew Wen Chin
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Principal Component ◽  
Lyapunov Stability Theory ◽  
Recognition System ◽  
Lyapunov Theory ◽  
Neural Network Approach ◽  
Linear Discriminant ◽  
Feature Based ◽  
Definition Of

This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems.

scholarly journals OPTIMIZATION OF EVALUATION OF THE INFORMATIVITY OF MEDICAL INDICATORS ON THE BASIS OF THE HYBRID APPROACH

2017 ◽  
pp. 108-115
Author(s):  
Є.В. БОДЯНСЬКИЙ ◽  
І.Г. ПЕРОВА ◽  
Г.В. СТОЙКА
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Hybrid Approach ◽  
Principal Component ◽  
Mining Area ◽  
Extraction Methods ◽  
Optimal Combination ◽  
Information Quantity ◽  
New Approach

Feature Selection task is one of most complicated and actual in Data Mining area. Any approaches for it solving are based on non-mathematical and presentative hypothesis. New approach for evaluation of medical features information quantity, based on optimal combination of Feature Selection and Feature Extraction methods. This approach permits to produce optimal reduced number of features with linguistic interpreting of each ones. Hybrid system of Feature Selection/Extraction is proposed. This system is numerically simple, can produce Feature Selection/ Extraction with any number of features using standard method of principal component analysis and calculating distance between first principal component and all medical features.

Score-Based Fusion in Multi-Unit Biometric Recognition System

2011 ◽  
Vol 48-49 ◽  
pp. 1010-1013 ◽  
Author(s):  
Yong Li ◽  
Jian Ping Yin ◽  
En Zhu
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Fusion Model ◽  
Biometric Recognition ◽  
Score Level Fusion ◽  
Fusion Rules ◽  
Biometric Systems ◽  
Score Fusion ◽  
Multiple Units ◽  
Level Fusion

The performance of biometric systems can be improved by combining multiple units through score level fusion. In this paper, different fusion rules based on match scores are comparatively studied for multi-unit fingerprint recognition. A novel fusion model for multi-unit system is presented first. Based on this model, we analyze the five common score fusion rules: sum, max, min, median and product. Further, we propose a new method: square. Note that the performance of these strategies can complement each other, we introduce the mixed rule: square-sum. We prove that square-sum rule outperforms square and sum rules. The experimental results show good performance of the proposed methods.

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