scholarly journals Neural Network Model for Face Recognition from Dynamic Vision Sensor

2020 ◽  
pp. short17-1-short17-8
Author(s):  
Fedor Shvetsov ◽  
Anton Konushin ◽  
Anna Sokolova
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Synthetic Data ◽  
Simulated Data ◽  
Fine Tuning ◽  
Vision Sensor ◽  
Dynamic Vision ◽  
The Face

In this work, we consider the applicability of the face recognition algorithms to the data obtained from a dynamic vision sensor. A basic method using a neural network model comprised of reconstruction, detection, and recognition is proposed that solves this problem. Various modifications of this algorithm and their influence on the quality of the model are considered. A small test dataset recorded on a DVS sensor is collected. The relevance of using simulated data and different approaches for its creation for training a model was investigated. The portability of the algorithm trained on synthetic data to the data obtained from the sensor with the help of fine-tuning was considered. All mentioned variations are compared to one another and also compared with conventional face recognition from RGB images on different datasets. The results showed that it is possible to use DVS data to perform face recognition with quality similar to that of RGB data.

scholarly journals Computer Assisted Chord Detection Using Deep Learning and YOLOV4 Neural Network Model

2021 ◽  
Vol 2083 (4) ◽  
pp. 042017
Author(s):  
Yingdong Ru
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Training Model ◽  
Experimental Results ◽  
Fine Tuning ◽  
Computer Assisted ◽  
Single Voice ◽  
Music Information ◽  
Chord Recognition

Abstract Music symbol recognition is an important part of Optical Music Recognition (OMR), Chord recognition is one of the most important research contents in the field of music information retrieval. It plays an important role in information processing, music structure analysis, and recommendation systems. Aiming at the problem of low chord recognition accuracy in the OMR recognition model, the article proposes a chord recognition method based on the YOLOV4 neural network model. First, the YOLOV4 network model is used to train single-voice scores to obtain the best training model. Then, the scores containing chords are trained through neural network fine-tuning technology. The experimental results show that the method recognizes the chords with great results, the model was tested on the test set generated by MuseScore. The experimental results show that the accuracy of note recognition is high, which can reach the accuracy of duration value of 0.96 which is higher than the accuracy of note recognition of other score recognition models.

A mask recognition algorithm based on improved YOLO-V4 neural network (Preprint)

2021 ◽  
Author(s):  
Song Zhang ◽  
Shaoqiang Wang ◽  
Shaoqiang Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Target Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Prevention And Control ◽  
Preventive Measures ◽  
Recognition Algorithm ◽  
Facial Features ◽  
And Control

BACKGROUND With the spread of the new crown virus, the wearing of masks as one of the effective preventive measures is getting more and more attention, and the behavior of not wearing a mask is likely to cause the spread of the virus, which is not conducive to the prevention and control of the epidemic. OBJECTIVE In this paper, a new neural network model is used to better recognize the facial features of people with exit masks. METHODS This paper proposes a mask recognition algorithm based on improved YOLO-V4 neural network that can solve this problem well. This paper integrates SE-Net and DenseNet network as the reference neural network of YOLO-V4 and introduces deformable convolution. RESULTS Compared with other target detection networks, the improved YOLO-V4 neural network used in this paper improves the accuracy of mask detection to a certain extent. CONCLUSIONS The improved YOLO-V4 network proposed in this article has verified its feasibility and accuracy through experiments and has great value in use. Improving the YOLO-V4 network can help better respond to face recognition with masks in the epidemic. However, the model studied in this article focuses on accuracy and is slightly lacking in speed. The next step is to increase its speed based on ensuring accuracy and consider actual deployment and use.

scholarly journals Complex imaging of phase domains by deep neural networks

IUCrJ ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 12-21
Author(s):  
Longlong Wu ◽  
Pavol Juhas ◽  
Shinjae Yoo ◽  
Ian Robinson
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Single Particle ◽  
Deep Neural Network ◽  
Phase Retrieval ◽  
Particle Image ◽  
Synthetic Data ◽  
Accurate Estimate ◽  
Initial Guess

The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-retrieval methods, has been extensively applied in X-ray structural science. Particularly for strong-phase objects, such as the phase domains found inside crystals by Bragg coherent diffraction imaging (BCDI), conventional iteration methods are time consuming and sensitive to their initial guess because of their iterative nature. Here, a deep-neural-network model is presented which gives a fast and accurate estimate of the complex single-particle image in the form of a universal approximator learned from synthetic data. A way to combine the deep-neural-network model with conventional iterative methods is then presented to refine the accuracy of the reconstructed results from the proposed deep-neural-network model. Improved convergence is also demonstrated with experimental BCDI data.

scholarly journals A Deep Convolution Neural Network Model for Vehicle Recognition and Face Recognition

2017 ◽  
Vol 107 ◽  
pp. 715-720 ◽  
Author(s):  
Xingcheng Luo ◽  
Ruihan Shen ◽  
Jian Hu ◽  
Jianhua Deng ◽  
Linji Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Convolution Neural Network ◽  
Vehicle Recognition ◽  
Deep Convolution Neural Network

scholarly journals Neural Network Topology Construction and Classroom Interaction Benchmark Graph Based on Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Congcong Luan ◽  
Peng Shang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Video Recording ◽  
Classroom Interaction ◽  
Recognition Method ◽  
Convolutional Network ◽  
Status Analysis

With the rapid development of artificial intelligence and deep learning in recent years, many universities have put forward the goal of achieving digitalization, intelligent, and education informatization on campus. Throughout the lecture and learning process, the classroom status is an important reference factor to assess students’ acceptance of the course and the quality of lectures. However, at present, classroom status analysis is mainly conducted manually, which can distract teachers’ attention, so it is of great research significance to find a method that can improve the efficiency of classroom status analysis. In this paper, we choose an offline method to analyze the status of a classroom video recording in terms of students’ behavior and attendance in terms of frames, in which student behavior is identified by an improved target detection algorithm and attendance is analyzed by face recognition. By analyzing the structure of the neural network model, an improved neural network model is proposed for its characteristics of a large number of parameters and poor detection of small targets in the basic network. The backbone network is replaced by the improved neural network, and the depth-separable convolutional network is used to reduce the network parameters and increase the computation speed. The information in the deeper feature map is fused upward into the shallow layer to improve the accuracy of small target recognition. Finally, the optimization algorithm is incorporated into the network to optimize the network model and accelerate the model convergence speed. In addition, this paper incorporates the improved behavior recognition method and face recognition method into the system to realize the analysis of the offline classroom status. The system is divided into a teacher side and a management side, where the teacher side is responsible for uploading course recordings and the management side is responsible for randomly analyzing students’ status and attendance at any time, and the combination of the two forms a convenient and comprehensive classroom status analysis system platform. Users can upload classroom videos through the instructor interface and can view the classroom status analysis results of a course at any time by searching randomly in the administration. In this paper, the classroom status is mainly judged by the recognition of students’ behaviors.

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