Decision-level fusion approach to face recognition with multiple cameras

2014 ◽  
Author(s):  
Seokwon Yeom
Keyword(s):  
Face Recognition ◽  
Multiple Cameras ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion ◽  
Fusion Approach

scholarly journals Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2080 ◽  
Author(s):  
Jing Li ◽  
Tao Qiu ◽  
Chang Wen ◽  
Kai Xie ◽  
Fang-Qing Wen
Keyword(s):  
Face Recognition ◽  
Decision Level ◽  
Model Based ◽  
Decision Level Fusion ◽  
Robust Face Recognition ◽  
Level Fusion

Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

2019 ◽  
Vol 9 (4) ◽  
pp. 1-14
Author(s):  
Priti Shivaji Sanjekar ◽  
Jayantrao B. Patil
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Wavelet Transformation ◽  
Multimodal Biometrics ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Level Fusion ◽  
Fusion Approach ◽  
Match Score

Multimodal biometrics is the frontier to unimodal biometrics as it integrates the information obtained from multiple biometric sources at various fusion levels i.e. sensor level, feature extraction level, match score level, or decision level. In this article, fingerprint, palmprint, and iris are used for verification of an individual. The wavelet transformation is used to extract features from fingerprint, palmprint, and iris. Further the PCA is used for dimensionality reduction. The fusion of traits is employed at three levels: feature level; feature level combined with match score level; and feature level combined with decision level. The main objective of this research is to observe effect of combined fusion levels on verification of an individual. The performance of three cases of fusion is measured in terms of EER and represented with ROC. The experiments performed on 100 different subjects from publicly available databases demonstrate that combining feature level with match score level and feature level with decision level fusion both outperforms fusion at only a feature level.

Decision Level Fusion

2011 ◽  
pp. 328-348 ◽  
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Face Recognition ◽  
Fusion Rule ◽  
Fusion Rules ◽  
Decision Level ◽  
Classifier Selection ◽  
Selection Approach ◽  
Decision Level Fusion ◽  
Level Fusion ◽  
Biometric Fusion

With this chapter, we first present a variety of decision level fusion rules and classifier selection approaches, and then show a case study of face recognition based on decision level fusion, and finally offer a summary of three levels of biometric fusion technologies. In a multi-biometric system, classifier selection techniques may be associated with the decision level fusion as follows: classifier selection is first carried out to select a number of classifiers from all classifier candidates. Then the selected classifiers make their own decisions and the decision level fusion rule is used to integrate the multiple decisions to produce the final decision. As a result, in this chapter, we also introduce classifier selection by showing a classifier selection approach based on correlation analysis. This chapter is organized as follows. Section 15.1 provides an introduction to decision level fusion. Section 15.2 presents several simple and popular decision level fusion rules such as the AND, OR, RANDOM, Voting rules, as well as the weighted majority decision rule. Section 15.3 introduces a classifier selection approach based on correlations between classifiers. Section 15.4 presents a case study of group decision-based face recognition. Finally, Section 15.5 offers some comments on three levels of biometric fusion.

scholarly journals A Multimodal Facial Emotion Recognition Framework through the Fusion of Speech with Visible and Infrared Images

2020 ◽  
Vol 4 (3) ◽  
pp. 46
Author(s):  
Mohammad Faridul Haque Siddiqui ◽  
Ahmad Y. Javaid
Keyword(s):  
Emotion Recognition ◽  
Final Decision ◽  
Infrared Images ◽  
Decision Level ◽  
Visible Images ◽  
Contemporary Work ◽  
Decision Level Fusion ◽  
Hybrid Fusion ◽  
Level Fusion ◽  
Fusion Approach

The exigency of emotion recognition is pushing the envelope for meticulous strategies of discerning actual emotions through the use of superior multimodal techniques. This work presents a multimodal automatic emotion recognition (AER) framework capable of differentiating between expressed emotions with high accuracy. The contribution involves implementing an ensemble-based approach for the AER through the fusion of visible images and infrared (IR) images with speech. The framework is implemented in two layers, where the first layer detects emotions using single modalities while the second layer combines the modalities and classifies emotions. Convolutional Neural Networks (CNN) have been used for feature extraction and classification. A hybrid fusion approach comprising early (feature-level) and late (decision-level) fusion, was applied to combine the features and the decisions at different stages. The output of the CNN trained with voice samples of the RAVDESS database was combined with the image classifier’s output using decision-level fusion to obtain the final decision. An accuracy of 86.36% and similar recall (0.86), precision (0.88), and f-measure (0.87) scores were obtained. A comparison with contemporary work endorsed the competitiveness of the framework with the rationale for exclusivity in attaining this accuracy in wild backgrounds and light-invariant conditions.

Sign in / Sign up

Export Citation Format

Share Document