Maximum weight multi-modal information fusion algorithm of electroencephalographs and face images for emotion recognition

2021 ◽  
Vol 94 ◽  
pp. 107319
Author(s):  
Mei Wang ◽  
Ziyang Huang ◽  
Yuancheng Li ◽  
Lihong Dong ◽  
Hongguang Pan
Keyword(s):  
Emotion Recognition ◽  
Information Fusion ◽  
Maximum Weight ◽  
Fusion Algorithm ◽  
Face Images

An Information Fusion Algorithm Based on Kalman Filtering

2013 ◽  
Vol 444-445 ◽  
pp. 1072-1076
Author(s):  
Xiu Hu Tan
Keyword(s):  
Mathematical Model ◽  
Information Fusion ◽  
Minimum Mean Square Error ◽  
Minimum Variance ◽  
Probability Method ◽  
Fusion Algorithm ◽  
Multisensor Systems ◽  
Optimal Information ◽  
Noise Statistics ◽  
The Mathematical Model

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

scholarly journals Multi-sensor Information Fusion Method Based on BP Neural Network

2016 ◽  
Vol 12 (05) ◽  
pp. 53 ◽  
Author(s):  
Lin Liandong
Keyword(s):  
Neural Network ◽  
Bayesian Inference ◽  
Information Fusion ◽  
System Development ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fusion Algorithm ◽  
Temperature Detection ◽  
Posterior Expectation ◽  
Sensor Information

This study aims to solve the problem of multi-sensor information fusion, which is a key issue in the multi-sensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.

scholarly journals A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

2020 ◽  
Vol 17 (3) ◽  
pp. 1562
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Gabor Filter ◽  
Regression Line ◽  
Histogram Equalization ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fusion Algorithm ◽  
Decision Level ◽  
Face Images ◽  
Level Method

<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

scholarly journals Audiovisual Emotion Recognition Using Entropy-estimation-based Multimodal Information Fusion

2021 ◽  
Author(s):  
Zhibing Xie
Keyword(s):  
Human Computer Interaction ◽  
Emotion Recognition ◽  
Information Fusion ◽  
Score Level Fusion ◽  
Information Theoretic ◽  
Multimodal Information Fusion ◽  
Multimodal Information ◽  
Feature Level Fusion ◽  
Level Fusion ◽  
Computer Interaction

Understanding human emotional states is indispensable for our daily interaction, and we can enjoy more natural and friendly human computer interaction (HCI) experience by fully utilizing human’s affective states. In the application of emotion recognition, multimodal information fusion is widely used to discover the relationships of multiple information sources and make joint use of a number of channels, such as speech, facial expression, gesture and physiological processes. This thesis proposes a new framework of emotion recognition using information fusion based on the estimation of information entropy. The novel techniques of information theoretic learning are applied to feature level fusion and score level fusion. The most critical issues for feature level fusion are feature transformation and dimensionality reduction. The existing methods depend on the second order statistics, which is only optimal for Gaussian-like distributions. By incorporating information theoretic tools, a new feature level fusion method based on kernel entropy component analysis is proposed. For score level fusion, most previous methods focus on predefined rule based approaches, which are usually heuristic. In this thesis, a connection between information fusion and maximum correntropy criterion is established for effective score level fusion. Feature level fusion and score level fusion methods are then combined to introduce a two-stage fusion platform. The proposed methods are applied to audiovisual emotion recognition, and their effectiveness is evaluated by experiments on two publicly available audiovisual emotion databases. The experimental results demonstrate that the proposed algorithms achieve improved performance in comparison with the existing methods. The work of this thesis offers a promising direction to design more advanced emotion recognition systems based on multimodal information fusion and has great significance to the development of intelligent human computer interaction systems.

An Information Fusion Algorithm Based on Chirplet-Hough Transformation

2012 ◽  
Vol 7 (19) ◽  
pp. 426-433 ◽  
Author(s):  
Deng Minghui ◽  
Zeng Qingshuang ◽  
Zhang Lanying
Keyword(s):  
Information Fusion ◽  
Fusion Algorithm ◽  
Hough Transformation

Sensor management based on collaborative information fusion algorithm for gas source localisation

2018 ◽  
Vol 28 (4) ◽  
pp. 242
Author(s):  
Yong Zhang ◽  
Liyi Zhang ◽  
Jianfeng Han ◽  
Yi Yang ◽  
Xinyuan Ma
Keyword(s):  
Information Fusion ◽  
Sensor Management ◽  
Fusion Algorithm ◽  
Source Localisation ◽  
Gas Source
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