scholarly journals The Development of Long-Distance Viewing Direction Analysis and Recognition of Observed Objects Using Head Image and Deep Learning

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1880
Author(s):  
Yu-Shiuan Tsai ◽  
Nai-Chi Chen ◽  
Yi-Zeng Hsieh ◽  
Shih-Syun Lin
Keyword(s):  
Facial Feature ◽  
Line Of Sight ◽  
Long Distance ◽  
Data Set ◽  
Detection Technology ◽  
The Neural Network ◽  
Single Lens ◽  
The Face ◽  
Test Face ◽  
Better Than

In this study, we use OpenPose to capture many facial feature nodes, create a data set and label it, and finally bring in the neural network model we created. The purpose is to predict the direction of the person’s line of sight from the face and facial feature nodes and finally add object detection technology to calculate the object that the person is observing. After implementing this method, we found that this method can correctly estimate the human body’s form. Furthermore, if multiple lenses can get more information, the effect will be better than a single lens, evaluating the observed objects more accurately. Furthermore, we found that the head in the image can judge the direction of view. In addition, we found that in the case of the test face tilt, approximately at a tilt angle of 60 degrees, the face nodes can still be captured. Similarly, when the inclination angle is greater than 60 degrees, the facing node cannot be used.

scholarly journals Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 191-196
Author(s):  
Yun Jie Li ◽  
Hui Song
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Daily Living ◽  
Tree Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Daily Living Activity ◽  
The Neural Network ◽  
Better Than

In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

scholarly journals Enhancing the feature-based 3D deformable face recognition using hybrid PCA-NN

2021 ◽  
Vol 22 (1) ◽  
pp. 215
Author(s):  
Cahyo Darujati ◽  
Supeno Mardi Susiki Nugroho ◽  
Deny Kurniawan ◽  
Mochamad Hariadi
Keyword(s):  
Graphical Model ◽  
Original Learning ◽  
Principal Component ◽  
Facial Feature ◽  
Theory Approach ◽  
Face Features ◽  
The Neural Network ◽  
The Face ◽  
Critical Components ◽  
Almost All

<p>Facial recognition is one of the most important advancements in image processing. An important job is to build an automated framework with the same human capacity’s for recognizing face. The face is a complex 3D graphical model, and constructing a computational model is a challenging task. This paper aims at a facial detection technique focused on the coding and decoding of the facial feature object theory approach to data. One of the most natural and common principal component analysis (PCA) method. This approach transforms the face features into a minimal set of basic attributes, peculiarities, which are the critical components of the original learning image collection (or the training package). The proposed technique is a combination of the PCA system and the identification of components using the neural network (NN) feed-forward propagation method. This experiment proves that recognition of deformed 3D face is doable. By taking into account almost all forms of feature extraction and engineering, the NN yields a recognition score of 95%.</p>

Real-Time Pedestrian Detection Using Convolutional Neural Networks

2018 ◽  
Vol 32 (11) ◽  
pp. 1856014 ◽  
Author(s):  
Ping Kuang ◽  
Tingsong Ma ◽  
Fan Li ◽  
Ziwei Chen
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Original Network ◽  
Data Set ◽  
Great Opportunity ◽  
Detection Technology ◽  
Proposed Model ◽  
Secure Environment ◽  
Definition Of ◽  
Better Than

Pedestrian detection provides manager of a smart city with a great opportunity to manage their city effectively and automatically. Specifically, pedestrian detection technology can improve our secure environment and make our traffic more efficient. In this paper, all of our work both modification and improvement are made based on YOLO, which is a real-time Convolutional Neural Network detector. In our work, we extend YOLO’s original network structure, and also give a new definition of loss function to boost the performance for pedestrian detection, especially when the targets are small, and that is exactly what YOLO is not good at. In our experiment, the proposed model is tested on INRIA, UCF YouTube Action Data Set and Caltech Pedestrian Detection Benchmark. Experimental results indicate that after our modification and improvement, the revised YOLO network outperforms the original version and also is better than other solutions.

Facial Emotions Recognition Using Deep Learning Technology

2021 ◽  
Vol 9 (9) ◽  
pp. 1245-1249
Author(s):  
Abhinav Chaubey
Keyword(s):  
Data Augmentation ◽  
Facial Feature ◽  
Face Image ◽  
Facial Emotion ◽  
Learning Technology ◽  
Model Data ◽  
Data Set ◽  
Artificial Intelligent ◽  
Facial Emotions ◽  
The Face

Abstract: Artificial Intelligent give us capability to detect emotions of human being. Due variation of individual expression it is difficult to find precisely. With AI we can mimics a human's capability like recognising someone with a restricted facial feature. this . paper, of mine indentify the face emotions by detecting areas of face like eyes, nose, lips, and forehead. By implementing two repressing methods like histogram and data augmentation we propos to extract characteristics of facial emotion. Here in this paper two dimensional architecture is used. First is used for inputting greyscale of face image where as second is for accepting histograms. the final process calculate the result on the bases of KNN and SVM classifiers. The results indicates that proposed algorithm detect six fundamental facial emotions , Happiness, Anger, Fear and surprise. Précised result are expected by using trained model data set. Keywords: SVM, KNN, FER, DNN, VGG16, HOG, HSOG.

Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing

2021 ◽  
Vol 12 (3) ◽  
pp. 27-43
Author(s):  
Liu Jiasen ◽  
Wang Xu An ◽  
Chen Bowei ◽  
Tu Zheng ◽  
Zhao Kaiyang
Keyword(s):  
Face Recognition ◽  
Homomorphic Encryption ◽  
Facial Feature ◽  
Facial Features ◽  
User Privacy ◽  
Data Set ◽  
Maximum Value ◽  
Cloud Server ◽  
The Face ◽  
Cloud Servers

With the enhancement of the performance of cloud servers, face recognition applications are becoming more and more popular, but it also has some security problems, such as user privacy data leakage. This article proposes a face recognition scheme based on homomorphic encryption in cloud environment. The article first uses the MTCNN algorithm to detect face and correct the data and extracts the face feature vector through the FaceNet algorithm. Then, the article encrypts the facial features with the CKKS homomorphic encryption scheme and builds a database of the encrypted facial feature in the cloud server. The process of face recognition is as follows: calculate the distance between the encrypted feature vectors and the maximum value of the ciphertext result, decrypt it, and compare the threshold to determine whether it is a person. The experimental results show that when the scheme is based on the LFW data set, the threshold is 1.1236, and the recognition accuracy in the ciphertext is 94.8837%, which proves the reliability of the proposed scheme.

scholarly journals Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 194-210 ◽  
Author(s):  
Marino Marrocu ◽  
Luca Massidda
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
State Of The Art ◽  
Performance Comparison ◽  
Data Set ◽  
Radar Images ◽  
The Neural Network ◽  
Meteorological Radar ◽  
One Year ◽  
Better Than

In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering an area of about 104 km2 over Japan, and a period of five years with a sampling frequency of five minutes. The performance of the neural network, trained with three of the five years of data, forecasts with a time horizon of up to one hour, evaluated over one year of the data, proved to be significantly better than those obtained with the techniques currently in use.

A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit

2019 ◽  
Vol 70 (3) ◽  
pp. 184-192
Author(s):  
Toan Dao Thanh ◽  
Vo Thien Linh
Keyword(s):  
Processing System ◽  
Warning System ◽  
Raspberry Pi ◽  
Data Set ◽  
Lower Lip ◽  
Driver Drowsiness ◽  
Image Sensing ◽  
The Face ◽  
The Common ◽  
University Laboratory

In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”

Evaluation for estimating of the PDF and the CDF of Generalized Inverted Exponential Distribution with Application in Industry

2020 ◽  
pp. 507-522
Author(s):  
Parisa Torkaman
Keyword(s):  
Least Squares ◽  
Exponential Distribution ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Real Data ◽  
Minimum Variance ◽  
Cumulative Distribution ◽  
Estimation Methods ◽  
Data Set ◽  
Better Than

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

The Alternatives to War

2018 ◽  
Author(s):  
James Pattison
Keyword(s):  
Just War ◽  
Just War Theory ◽  
Economic Sanctions ◽  
Responsibility To Protect ◽  
Military Action ◽  
Comprehensive Account ◽  
The Face ◽  
Normative Issues ◽  
Better Than

If states are not to go to war, what should they do instead? In The Alternatives to War: From Sanctions to Non-violence, James Pattison considers the case for the alternatives to military action to address mass atrocities and aggression. He covers the normative issues raised by measures ranging from comprehensive economic sanctions, diplomacy, and positive incentives, to criminal prosecutions, non-violent resistance, accepting refugees, and arming rebels. For instance, given the indiscriminateness of many sanctions regimes, are sanctions any better than war? Should states avoid ‘megaphone diplomacy’ and adopt more subtle measures? What, if anything, can non-violent methods such as civilian defence and civilian peacekeeping do in the face of a ruthless opponent? Is it a serious concern that positive incentives can appear to reward aggressors? Overall, Pattison provides a comprehensive account of the ethics of the alternatives to war. In doing so, he argues that the case for war is weaker and the case for many of the alternatives is stronger than commonly thought. The upshot is that, when reacting to mass atrocities and aggression, states are generally required to pursue the alternatives to war rather than military action. Pattison concludes that this has significant implications for pacifism, Just War Theory, and the responsibility to protect doctrine.

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