An Improved Feature-Level Fusion Algorithm for Multimodal Biometrics Recognition Technology

2012 ◽  
Vol 3 (2) ◽  
pp. 8-14
Author(s):  
Xiaona Xu
Keyword(s):  
Multimodal Biometrics ◽  
Fusion Algorithm ◽  
Feature Level Fusion ◽  
Biometrics Recognition ◽  
Level Fusion

Face and palmprint feature level fusion for single sample biometrics recognition

2007 ◽  
Vol 70 (7-9) ◽  
pp. 1582-1586 ◽  
Author(s):  
Yong-Fang Yao ◽  
Xiao-Yuan Jing ◽  
Hau-San Wong
Keyword(s):  
Single Sample ◽  
Feature Level Fusion ◽  
Biometrics Recognition ◽  
Level Fusion

scholarly journals A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ujwalla Gawande ◽  
Mukesh Zaveri ◽  
Avichal Kapur
Keyword(s):  
Learning Algorithm ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Rejection Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Fusion Algorithm ◽  
Biometric Systems ◽  
Feature Level Fusion ◽  
Level Fusion

Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.

scholarly journals Hybridization of Feature Level Fusion with Ant Colony Optimization in Multimodal Biometrics

2019 ◽  
Vol 8 (6) ◽  
pp. 2846-2851
Keyword(s):  
Ant Colony Optimization ◽  
Random Search ◽  
Ant Colony ◽  
Memory Storage ◽  
Morphological Operations ◽  
Np Hard ◽  
Biometric System ◽  
Multimodal Biometrics ◽  
Feature Level Fusion ◽  
Level Fusion

In biometric system, multimodal biometrics provides stronger security as compared to unimodal biometrics. Even though multimodal biometric improves the accuracy and reliability of the system, but requires large memory storage and consumes numerous execution time due to use of high dimensionality datasets. Search is being an NP-hard problem in biometrics, which garnish an attention for research in biometric system. Due to NP-hard nature of searching in biometric, accurate solutions could not be discovered in limited time. Therefore, researchers use heuristic or random search methods such as PSO, GA, ACO and Cuckoo search etc. to obtain optimal or approximate optimal solutions for such problems. This paper proposes a hybrid approach of feature level fusion in biometric system with Ant Colony Optimization based feature sub selection method to aiming to improve performance. The median filter and morphological operations are used for pre-processing of finger vein and fingerprint images respectively. Confusion matrix plot with equal error rate and accuracy are the evaluation parameters.

Feature Level Fusion of Face, Profile Face and Ear for Efficient Multimodal Biometrics

2020 ◽  
Vol 5 (2) ◽  
pp. 9-15
Author(s):  
Shweta Singh ◽  
Ravi Jaiswal ◽  
Siddharth Srivastava
Keyword(s):  
Multimodal Biometrics ◽  
Feature Level Fusion ◽  
Level Fusion
Sign in / Sign up

Export Citation Format

Share Document