Score Level Fusion Scheme in Hybrid Multibiometric System

2015 ◽  
pp. 166-177 ◽  
Author(s):  
Saliha Artabaz ◽  
Layth Sliman ◽  
Karima Benatchba ◽  
Hachemi Nabil Dellys ◽  
Mouloud Koudil
Keyword(s):  
Score Level Fusion ◽  
Fusion Scheme ◽  
Level Fusion

Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

2014 ◽  
Vol 23 (3) ◽  
pp. 033019 ◽  
Author(s):  
Fei He ◽  
Yuanning Liu ◽  
Xiaodong Zhu ◽  
Chun Huang ◽  
Ye Han ◽  
...  
Keyword(s):  
Score Level Fusion ◽  
Gabor Features ◽  
Fusion Scheme ◽  
Level Fusion

scholarly journals MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yanping Zhang ◽  
Jing Peng ◽  
Xiaohui Yuan ◽  
Lisi Zhang ◽  
Dongzi Zhu ◽  
...  
Keyword(s):  
Prunus Avium ◽  
Cultivar Identification ◽  
Leaf Shape ◽  
Persistent Homology ◽  
Growth Period ◽  
Image Features ◽  
Crop Species ◽  
Score Level Fusion ◽  
Level Fusion ◽  
Leaf Image

AbstractRecognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online.

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.

Multibiometrics Enhancement Using Quality Measurement in Score Level Fusion

2017 ◽  
pp. 260-267 ◽  
Author(s):  
Saliha Artabaz ◽  
Layth Sliman ◽  
Hachemi Nabil Dellys ◽  
Karima Benatchba ◽  
Mouloud Koudil
Keyword(s):  
Quality Measurement ◽  
Score Level Fusion ◽  
Level Fusion

i-Vector-Based Speaker Verification on Limited Data Using Fusion Techniques

2018 ◽  
Vol 29 (1) ◽  
pp. 565-582
Author(s):  
T.R. Jayanthi Kumari ◽  
H.S. Jayanna
Keyword(s):  
Linear Prediction ◽  
Vocal Tract ◽  
Speaker Verification ◽  
Excitation Source ◽  
Limited Data ◽  
Score Level Fusion ◽  
Verification System ◽  
Modeling Techniques ◽  
Prediction Residual ◽  
Level Fusion

Abstract In many biometric applications, limited data speaker verification plays a significant role in practical-oriented systems to verify the speaker. The performance of the speaker verification system needs to be improved by applying suitable techniques to limited data condition. The limited data represent both train and test data duration in terms of few seconds. This article shows the importance of the speaker verification system under limited data condition using feature- and score-level fusion techniques. The baseline speaker verification system uses vocal tract features like mel-frequency cepstral coefficients, linear predictive cepstral coefficients and excitation source features like linear prediction residual and linear prediction residual phase as features along with i-vector modeling techniques using the NIST 2003 data set. In feature-level fusion, the vocal tract features are fused with excitation source features. As a result, on average, equal error rate (EER) is approximately equal to 4% compared to individual feature performance. Further in this work, two different types of score-level fusion are demonstrated. In the first case, fusing the scores of vocal tract features and excitation source features at score-level-maintaining modeling technique remains the same, which provides an average reduction approximately equal to 2% EER compared to feature-level fusion performance. In the second case, scores of the different modeling techniques are combined, which has resulted in EER reduction approximately equal to 4.5% compared with score-level fusion of different features.

scholarly journals A Score-Level Fusion of Fingerprint Matching With Fingerprint Liveness Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 183391-183400
Author(s):  
Yongliang Zhang ◽  
Chenhao Gao ◽  
Shengyi Pan ◽  
Zhiwei Li ◽  
Yuanyang Xu ◽  
...  
Keyword(s):  
Fingerprint Matching ◽  
Score Level Fusion ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Level Fusion
Sign in / Sign up

Export Citation Format

Share Document