Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existingNhamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

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Improving the Accuracy of a Score Fusion Approach Based on Likelihood Ratio in Multimodal Biometric Systems

Image Analysis and Processing – ICIAP 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04146-4_55 ◽

2009 ◽

pp. 509-518 ◽

Cited By ~ 3

Author(s):

Emanuela Marasco ◽

Carlo Sansone

Keyword(s):

Likelihood Ratio ◽

Biometric Systems ◽

Score Fusion ◽

Fusion Approach ◽

Multimodal Biometric Systems

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BPN Based Likelihood Ratio Score Fusion for Audio-Visual Speaker Identification in Response to Noise

ISRN Artificial Intelligence ◽

10.1155/2014/737814 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13

Author(s):

Md. Rabiul Islam ◽

Md. Abdus Sobhan

Keyword(s):

Neural Network ◽

Likelihood Ratio ◽

Speaker Identification ◽

Back Propagation ◽

Audio Signal ◽

Artificial Noise ◽

Facial Features ◽

Ratio Score ◽

Speech Database ◽

Score Fusion

This paper deals with a new and improved approach of Back-propagation learning neural network based likelihood ratio score fusion technique for audio-visual speaker Identification in various noisy environments. Different signal preprocessing and noise removing techniques have been used to process the speech utterance and LPC, LPCC, RCC, MFCC, ΔMFCC and ΔΔMFCC methods have been applied to extract the features from the audio signal. Active Shape Model has been used to extract the appearance and shape based facial features. To enhance the performance of the proposed system, appearance and shape based facial features are concatenated and Principal Component Analysis method has been used to reduce the dimension of the facial feature vector. The audio and visual feature vectors are then fed to Hidden Markov Model separately to find out the log-likelihood of each modality. The reliability of each modality has been calculated using reliability measurement method. Finally, these integrated likelihood ratios are fed to Back-propagation learning neural network algorithm to discover the final speaker identification result. For measuring the performance of the proposed system, three different databases, that is, NOIZEUS speech database, ORL face database and VALID audio-visual multimodal database have been used for audio-only, visual-only, and audio-visual speaker identification. To identify the accuracy of the proposed system with existing techniques under various noisy environment, different types of artificial noise have been added at various rates with audio and visual signal and performance being compared with different variations of audio and visual features.

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Robustness analysis of likelihood ratio score fusion rule for multimodal biometric systems under spoof attacks

2011 Carnahan Conference on Security Technology ◽

10.1109/ccst.2011.6095935 ◽

2011 ◽

Cited By ~ 10

Author(s):

Zahid Akhtar ◽

Giorgio Fumera ◽

Gian Luca Marcialis ◽

Fabio Roli

Keyword(s):

Likelihood Ratio ◽

Robustness Analysis ◽

Fusion Rule ◽

Ratio Score ◽

Biometric Systems ◽

Score Fusion ◽

Multimodal Biometric Systems

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A Wavelet-CNN Feature Fusion Approach for Detecting COVID-19 from Chest Radiographs

2020 11th International Conference on Electrical and Computer Engineering (ICECE) ◽

10.1109/icece51571.2020.9393085 ◽

2020 ◽

Author(s):

Md. Latifur Rahman ◽

Nusrat Binta Nizam ◽

Prasun Datta ◽

Md. Moynul Hasan ◽

Taufiq Hasan ◽

...

Keyword(s):

Feature Fusion ◽

Chest Radiographs ◽

Fusion Approach

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Multimodal score level fusion for recognition using face and palmprint

International Journal of Electrical Engineering Education ◽

10.1177/0020720920929662 ◽

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.

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