Implementation of facial emotion recognition using cnn on jetson TX2

In this paper, a convolutional neural network (CNN), one of the most popular deep learning architectures used for facial extraction research, has been implemented on NVIDIA Jetson TX2 hardware. Different from many existing approaches investigating CNN with complex structure and large parameters, we have focused on building a robust neural network through extensive performance comparison and evaluation. In addition, we have collected a dataset using a built-in camera on a laptop computer. Specifically, we have applied our model on Jetson TX2 hardware to take advantage of the computational power of the embedded GPU to optimize computation time and data training. In particular, both FER2013 and RAF datasets with seven basic emotions have been used for training and testing purposes. Finally, the evaluation results show that the proposed method achieves an accuracy of up to 72% on the testing dataset.

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Automatic Development of Deep Learning Architectures for Image Segmentation

Sustainability ◽

10.3390/su12229707 ◽

2020 ◽

Vol 12 (22) ◽

pp. 9707

Author(s):

Sergiu Cosmin Nistor ◽

Tudor Alexandru Ileni ◽

Adrian Sergiu Dărăbant

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Deep Neural Networks ◽

Computation Time ◽

Network Architectures ◽

Automatic Method ◽

Learning Architectures ◽

Complex Dataset

Machine learning is a branch of artificial intelligence that has gained a lot of traction in the last years due to advances in deep neural networks. These algorithms can be used to process large quantities of data, which would be impossible to handle manually. Often, the algorithms and methods needed for solving these tasks are problem dependent. We propose an automatic method for creating new convolutional neural network architectures which are specifically designed to solve a given problem. We describe our method in detail and we explain its reduced carbon footprint, computation time and cost compared to a manual approach. Our method uses a rewarding mechanism for creating networks with good performance and so gradually improves its architecture proposals. The application for the algorithm that we chose for this paper is segmentation of eyeglasses from images, but our method is applicable, to a larger or lesser extent, to any image processing task. We present and discuss our results, including the architecture that obtained 0.9683 intersection-over-union (IOU) score on our most complex dataset.

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Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural Network

IETE Journal of Research ◽

10.1080/03772063.2021.1902868 ◽

2021 ◽

pp. 1-20

Author(s):

P. M. Ashok Kumar ◽

Jeevan Babu Maddala ◽

K. Martin Sagayam

Keyword(s):

Neural Network ◽

Emotion Recognition ◽

Facial Emotion Recognition ◽

Facial Emotion ◽

Descriptor Selection ◽

Optimal Descriptor

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Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽

10.3390/electronics10151836 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1836

Author(s):

Bo-Hye Choi ◽

Donghwi Hwang ◽

Seung-Kwan Kang ◽

Kyeong-Yun Kim ◽

Hongyoon Choi ◽

...

Keyword(s):

Neural Network ◽

Attenuation Correction ◽

Complex Structure ◽

Scatter Correction ◽

Amyloid Β ◽

Correction Method ◽

Percentage Error ◽

Similarity Coefficients ◽

Brain Pet ◽

Positron Emission

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

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Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽

10.3390/math9101122 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1122

Author(s):

Oksana Mandrikova ◽

Nadezhda Fetisova ◽

Yuriy Polozov

Keyword(s):

Neural Network ◽

Time Series ◽

Hybrid Model ◽

Complex Structure ◽

Arima Model ◽

Ionospheric Parameter ◽

Arima Models ◽

Complicated Structure ◽

Threshold Functions ◽

Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Applied Sciences ◽

10.3390/app11062838 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2838

Author(s):

Nikitha Johnsirani Venkatesan ◽

Dong Ryeol Shin ◽

Choon Sung Nam

Keyword(s):

Neural Network ◽

Radiation Dose ◽

Convolutional Neural Network ◽

Model Performance ◽

Performance Comparison ◽

Apache Spark ◽

Training Time ◽

Learning Framework ◽

Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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Performance Comparison for Different Neural Network Architectures for chest X-Ray Image Classification

2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST) ◽

10.1109/iceast52143.2021.9426289 ◽

2021 ◽

Author(s):

Pinyada Rajadanuraks ◽

Sarapom Suranuntchai ◽

Suejit Pechprasam ◽

Treesukon Treebupachatsakul

Keyword(s):

Neural Network ◽

Image Classification ◽

Performance Comparison ◽

Network Architectures ◽

X Ray ◽

Chest X Ray ◽

Neural Network Architectures

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Real-time facial emotion recognition using lightweight convolution neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1827/1/012130 ◽

2021 ◽

Vol 1827 (1) ◽

pp. 012130

Author(s):

Qi Li ◽

Yun Qing Liu ◽

Yue Qi Peng ◽

Cong Liu ◽

Jun Shi ◽

...

Keyword(s):

Neural Network ◽

Emotion Recognition ◽

Real Time ◽

Convolution Neural Network ◽

Facial Emotion Recognition ◽

Facial Emotion

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Embedded Deep Learning Prototyping Approach for Cyber-Physical Systems: Smart LIDAR Case Study

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10010018 ◽

2021 ◽

Vol 10 (1) ◽

pp. 18

Author(s):

Quentin Cabanes ◽

Benaoumeur Senouci ◽

Amar Ramdane-Cherif

Keyword(s):

Neural Network ◽

Deep Learning ◽

Computation Time ◽

Physical World ◽

Cyber Physical Systems ◽

Physical Systems ◽

Research Technology ◽

Standard Design ◽

Hardware Architectures ◽

Voxel Grid

Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias.

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