To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introdu...To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.展开更多
Emotion recognition via facial expressions （ERFE） has attracted a great deal of interest with recent advances in artificial intelligence and pattern recognition. Most studies are based on 2D images, and their perfor...Emotion recognition via facial expressions （ERFE） has attracted a great deal of interest with recent advances in artificial intelligence and pattern recognition. Most studies are based on 2D images, and their performance is usually computationally expensive. In this paper, we propose a real-time emotion recognition approach based on both 2D and 3D facial expression features captured by Kinect sensors. To capture the deformation of the 3D mesh during facial expression, we combine the features of animation units （AUs） and feature point positions （FPPs） tracked by Kinect. A fusion algorithm based on improved emotional profiles （IEPs） arid maximum confidence is proposed to recognize emotions with these real-time facial expression features. Experiments on both an emotion dataset and a real-time video show the superior performance of our method.展开更多
基金Project(61403422)supported by the National Natural Science Foundation of ChinaProject(17C1084)supported by Hunan Education Department Science Foundation of ChinaProject(17ZD02)supported by Hunan University of Arts and Science,China
文摘To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.
基金Project＇supportedV by the National Natural Science Foundation of China （No. 61272211） and the Six Talent Peaks Project in Jiangsu Province of China （No. DZXX-026）
文摘Emotion recognition via facial expressions （ERFE） has attracted a great deal of interest with recent advances in artificial intelligence and pattern recognition. Most studies are based on 2D images, and their performance is usually computationally expensive. In this paper, we propose a real-time emotion recognition approach based on both 2D and 3D facial expression features captured by Kinect sensors. To capture the deformation of the 3D mesh during facial expression, we combine the features of animation units （AUs） and feature point positions （FPPs） tracked by Kinect. A fusion algorithm based on improved emotional profiles （IEPs） arid maximum confidence is proposed to recognize emotions with these real-time facial expression features. Experiments on both an emotion dataset and a real-time video show the superior performance of our method.
