scholarly journals Back-Propagation Neural Network Based Face Detection and Pose Estimation

2002 ◽  
Vol 9B (6) ◽  
pp. 853-862
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Pose Estimation ◽  
Back Propagation ◽  
Back Propagation Neural Network

Multi-Angle Face Detection Using Back Propagation Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 2411-2415
Author(s):  
Kamarul Hawari Bin Ghazali ◽  
Jie Ma ◽  
Rui Xiao
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
System Development ◽  
Vision System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Machine Vision Application ◽  
Side Face ◽  
Fatigue Detection ◽  
Vision Application

In machine vision application, the main part to analyze an image is to identify its features which contribute to efficiency of the system. Many applications in vision system and image analysis used face detection as a feature of their whole system development. In application such as video surveillance, fatigue detection and security system, face is a fundamental step in the analysis before proceed to system implementation. It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. In this paper, we propose a neural network based approach to identify multi-angle face which falls into five categories: all left-side face, half left-side face, positive face, half right-side face, and all right-side face. More than 100 images of each category have been used for training and testing of face detection and its features was extracted to be an input to BP neural network. We analyzed the result of training and testing set of neural network and the best classification achieved was 90.7%.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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