scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

scholarly journals Face recognition based on full convolutional neural network based on transfer learning model

2021 ◽  
pp. 28-28
Author(s):  
Zhongkui Fan ◽  
Ye-Peng Guan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Test Accuracy ◽  
Great Success ◽  
Data Set ◽  
Face Features ◽  
The Face

Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.

Oversampling Based on Data Augmentation in Convolutional Neural Network for Silicon Wafer Defect Classification

2020 ◽  
Author(s):  
Uzma Batool ◽  
Mohd Ibrahim Shapiai ◽  
Nordinah Ismail ◽  
Hilman Fauzi ◽  
Syahrizal Salleh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Silicon Wafer ◽  
Data Augmentation ◽  
Imbalanced Data ◽  
Training Data ◽  
Defect Classification ◽  
Learning Method ◽  
Test Set

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals Face Recognition Model Using Back Propagation

2018 ◽  
Vol 7 (3.34) ◽  
pp. 237
Author(s):  
R Aswini Priyanka ◽  
C Ashwitha ◽  
R Arun Chakravarthi ◽  
R Prakash
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Face Identification ◽  
Identification System ◽  
Scientific World ◽  
The Face ◽  
Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.   

scholarly journals Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Ahmed Jawad A. AlBdairi ◽  
Zhu Xiao ◽  
Mohammed Alghaili
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Research Community ◽  
Facial Features ◽  
New Model ◽  
Image Dataset ◽  
Deep Cnn

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.

scholarly journals Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric

2021 ◽  
Vol 1 (2) ◽  
pp. 9-13
Author(s):  
Subrata Das ◽  
Sundaramurthy S ◽  
Aiswarya M ◽  
Suresh Jayaram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Textile Industry ◽  
Woven Fabric ◽  
Defect Identification ◽  
Complete Inspection ◽  
Hidden Layer ◽  
Deep Learning Neural Network

Inspection is the most important role in textile industry which declares the quality of the apparel product. Many Industries were improving their production or quality using Artificial Intelligence. Inspection of fabric in textile industry takes more time and labours. In order to reduce the number of labours and time taken to complete inspection, computerized image processing is done to identify the defects. It gives the accurate result in less time, thereby saves time and increases the production. The convolutional neural network in deep learning is mainly used for image processing for defect detection and classification. The high quality images are given as input, and then the images were used to train the deep learning neural network. Thewoven fabricdefects such as Holes, Selvedge tails, Stains, Wrong drawing and Snarlswere identified by using Convolutional Neural Network. The sample images were collected from the SkyCotex India Pvt.Ltd. The sample images were processed in CNN based machine learning ingoogle platform; the network has a input layer, n number of hidden layer and output layer. The neural network is trained and tested with the samples and the result obtained is used to calculate the efficiency of defect identification.

IMPLEMENTASI DEEP LEARNING MENGGUNAKAN FRAMEWORK TENSORFLOW DENGAN METODE FASTER REGIONAL CONVOLUTIONAL NEURAL NETWORK UNTUK PENDETEKSIAN JERAWAT

2018 ◽  
Vol 23 (2) ◽  
pp. 89-102
Author(s):  
Yunita Aulia Hasma ◽  
Widya Silfianti
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network

Jerawat sering dialami oleh kaum wanita maupun pria dari usia remaja hingga dewasa. Banyak rumah sakit dan klinik kecantikan yang dapat di datangi oleh para penderita untuk memeriksakan jerawat tersebut. Penelitian ini merupakan implementasi dari pendeteksian jerawat menggunakan image processing dan secara realtime, lalu sistem akan mengklasifikasikan jerawat yang ada pada wajah. Jerawat yang dapat dikenali oleh sistem ini yaitu jerawat, bekas, dan pus. Sistem deteksi dan klasifikasi ini dibuat dengan metode deep learning dengan menggunakan bahasa pemrograman Python, yang dibantu dengan menggunakan framework TensorFlow dengan model Faster R-CNN. Sistem ini hanya dapat berjalan di laptop dengan memiliki Python versi 3.6 di dalamnya dan telah memliki library Numpy, TkInter, Matplotlib, dan OpenCV dan juga memiliki kamera pada laptop yang digunakan agar dapat menjalankan sistem secara realtime yang didukung dengan GPU yang memadai. Perancangan alur aplikasi menggunakan flowchart diagram. Hasil uji terhadap sistem menggunakan perbandingan objek yang terdeteksi dengan yang seharusnya lalu dibagi dan dikalikan dengan seratus persen. Hasil yang didapat dari pengujian cukup baik menggunakan metode deep learning.

Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

2018 ◽  
Vol 41 (5) ◽  
pp. 1383-1394 ◽  
Author(s):  
Xuan Yao ◽  
Zhaobo Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sliding Mode Control ◽  
Convolutional Neural Network ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Trajectory Control ◽  
Active Magnetic Bearing ◽  
Learning Method ◽  
Mode Control

Active magnetic bearing (AMB) is competent in rotor trajectory control for potential applications such as mechanical processing and spindle attitude control, while the highly nonlinear and coupled dynamic characteristics especially in the condition of rotor large motion are obstacles in controller design. In this paper, a controller of AMB is proposed to achieve rotor 3D trajectory control. First, the dynamic model of the AMB-rotor system containing a nonlinear electromagnetic force model is introduced. Then the DCNN-SMC (deep convolutional neural network - sliding mode control) controller is proposed. Sliding mode control is used to achieve the tracking control with high robustness and responsiveness, and a deep convolutional neural network based on deep learning method is designed to compensate the uncertainties of the system. Finally, simulation of a 5-degree of freedom (DOF) system on various trajectories demonstrates evident control effect of the proposed controller in precision and significant effect of DCNN based on deep learning method in compensation control.

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