scholarly journals Developed artificial neural network based human face recognition

2019 ◽  
Vol 16 (3) ◽  
pp. 1279
Author(s):  
Maryam Mahmood Hussein ◽  
Ammar Hussein Mutlag ◽  
Hussain Shareef
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Soft Computing ◽  
Back Propagation ◽  
Human Interaction ◽  
Video Observation ◽  
Human Face Recognition ◽  
The Face ◽  
Artificial Neural

<p>Face recognition has become one of the most important challenging problems in personal computer-human interaction, video observation, and biometric. Many algorithms have been developed in the recent years. Theses algorithms are not sufficiently robust to address the complex images. Therefore, this paper proposes soft computing algorithm based face recognition. One of the most promising soft computing algorithms which is back-propagation artificial neural network (BP-ANN) has been proposed. The proposed BP-ANN has been developed to improve the performance of the face recognition. The implementation of the developed BP-ANN has been achieved using MATLAB environment. The developed BP-ANN requires supervised training to learn how to anticipate results from the desired data. The BP-ANN has been developed to recognition 10 persons. Ten images have been used for each person. Therefore, 100 images have been utilized to train the developed BP-ANN. In this research 50 images have been used for testing purpose. The results show that the developed BP-ANN has produced a success ratio of 82%.</p>

scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

Face Recognition Based on Artificial Neural Network: A Review

2021 ◽  
pp. 116-131
Author(s):  
Ameera Alblushi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Recognition System ◽  
Neural Network Approach ◽  
Pattern Recognition Systems ◽  
The Face ◽  
Artificial Neural ◽  
Facial Recognition System

The face recognition/detection is considered as one of the most popular applications in the field of image processing and biometric pattern recognition systems. Although the face recognition approach improves authentication procedure, nevertheless still many challenges appear due to diversities in human facial expression, image huge size, background complexity, variation in illumination, poses, blurry, etc. Therefore, the face detection procedure is classified as one of the most difficult tasks in computer vision. This research paper tends to address the concept of image processing along with the use of the Artificial Neural Network approach and represent it is a potential capability in enhancing the method of extracting face pattern through an adaption of various ANN topologies. Furthermore, it represents fundamental phases associated with the construction of any facial recognition system. Finally, it provides a general overview of different literature survives that related to face recognition based on the use of different ANN approaches and algorithms

scholarly journals Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah

2016 ◽  
Vol 6 (1) ◽  
pp. 30
Author(s):  
Nahdi Sabuari ◽  
Rizal Isnanto ◽  
Kusworo Adi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Image Data ◽  
Training Algorithm ◽  
Accuracy Rate ◽  
Average Accuracy ◽  
The Face ◽  
Artificial Neural

This research discusses about face detection and face recognition in an image. Face detection has only two classifications, i.e face and not face. Face recognition is compatible with some classifications of a number individuals who want to be recognized. Face detection and face recognition in thi study using Haar-Like Feature method and Artificial Neural Network Backpropagation. A method Haar-Like Feature used for detection and extraction in an image, because the clasification on this method showed success at used to detect image of the face. Artificial Neural Network Backpropagation is a training algorithm that is used to do training simulated on facial image data training stored in a database. This study uses Ms. Excel 2007 as database with 10 individual sample image, every image in each individuals having three distance with every range has four defferent light intensities, so that the data training stored in the database reached 120 data training. The results shows that the face detection and face recognition which is developed can recognize a face image with an average accuracy rate reaches 80,8% for each distance.

scholarly journals A soft computing approach to tunnel face stability in a probabilistic framework

2021 ◽  
Author(s):  
Enrico Soranzo ◽  
Carlotta Guardiani ◽  
Wei Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Support Pressure ◽  
Tunnel Face ◽  
Soft Computing Techniques ◽  
The Face ◽  
Tunnel Face Stability ◽  
Artificial Neural ◽  
Face Support

AbstractTunnel face is important for shallow tunnels to avoid collapses. In this study, tunnel face stability is studied with soft computing techniques. A database is created based on the literature which is used to train some broadly adopted soft computing techniques, ranging from linear regression to the artificial neural network. The soil dry density, cohesion, friction angle, cover depth and the tunnel diameter are used as the input parameters. The soft computing techniques state whether the face support is stable and predict the face support pressure. It is found that the artificial neural network outperforms the other techniques. The face support pressure is predicted with the artificial neural network for statistically distributed samples, and the failure probability is obtained with Monte Carlo simulations. In this way, the stability of the tunnel face can be reliably assessed and the support pressure can be estimated fairly accurately.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

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