scholarly journals A Novel Face Detection and Facial Feature Detection Algorithm using Skin Colour and Back Propagation Neural Network

2014 ◽  
Vol 90 (2) ◽  
pp. 28-31
Author(s):  
Pijush Chakraborty ◽  
Akashdeep Ghosh
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Feature Detection ◽  
Back Propagation ◽  
Facial Feature ◽  
Back Propagation Neural Network ◽  
Detection Algorithm ◽  
Skin Colour ◽  
Facial Feature Detection

A NOVEL APPROACH FOR ROBUST, FAST AND ACCURATE FACE DETECTION

2001 ◽  
Vol 09 (06) ◽  
pp. 769-779 ◽  
Author(s):  
SANUN SRISUK ◽  
WERASAK KURUTACH ◽  
KONGSAK LIMPITIKEAT
Keyword(s):  
Face Detection ◽  
Feature Detection ◽  
Accurate Result ◽  
Facial Feature ◽  
Facial Feature Detection ◽  
Novel Approach ◽  
Elliptical Model ◽  
Real Time Applications ◽  
Face Similarity ◽  
Human Faces

In this paper, we propose a novel approach for detecting human faces in a complex background scene. This method is robust and based on our enhanced hausdorff distance. A major aim of this research is to achieve a highly efficient method of face detection that can be used in any real time applications. In addition, our approach produces a very reliable and accurate result. The whole algorithm is composed of three main modules: robust skin detection using Fuzzy HSCC, face similarity measure using RAMHD, and facial feature detection using SVM. Moreover, a technique of automatically updating the size of an elliptical model is also introduced. The results will be shown with real images.

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%.

scholarly journals Random Forest Feature Selection and Back Propagation Neural Network to Detect Fire Using Video

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Jin-Xing Liang ◽  
Jian-Fu Zhao ◽  
Ning Sun ◽  
Bao-Jun Shi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Random Forest ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Fire Detection ◽  
Back Propagation Neural Network ◽  
Detection Algorithm ◽  
Low Sensitivity ◽  
Fire Recognition

As the most common serious disaster, fire may cause a lot of damages. Early detection and treatment of fires are of great significance to ensure public safety and to reduce losses caused by fires. However, traditional fire detectors are facing some focus issues such as low sensitivity and limited detection scenes. To overcome these problems, a video fire detection hybrid method based on random forest (RF) feature selection and back propagation (BP) neural network is proposed. The improved flame color model in RGB and HSI space and the visual background extractor (ViBe) in moving target detection algorithm are used to segment the suspected flame regions. Then, multidimensional features of flames are extracted from the suspected regions, and these extracted features are combined and selected according to the RF feature importance analysis. Finally, a BP neural network model is constructed for multifeature fusion and fire recognition. The test results on several experimental video sets show that the proposed method can effectively avoid feature interference and has an excellent recognition effect on fires in a variety of scenarios. The proposed method is applicable for fire recognition applied in video surveillance and detection robots.

scholarly journals Driver Drowsiness Detection Using Matlab

2019 ◽  
Author(s):  
Hanojhan Rajahrajasingh
Keyword(s):  
Eye Tracking ◽  
Face Detection ◽  
Template Matching ◽  
Feature Detection ◽  
Facial Feature ◽  
Specific Time ◽  
Facial Feature Detection ◽  
Driver Drowsiness ◽  
Falling Asleep ◽  
Threshold Time

When a driver doesn’t get proper rest, they fall asleep while driving and this leads to fatal accidents. This particular issue demands a solution in the form of a system that is capable of detecting drowsiness and to take necessary actions to avoid accidents.The detection is achieved with three main steps, it begins with face detection and facial feature detection using the famous Viola Jones algorithm followed by eye tracking. By the use of correlation coefficient template matching, the eyes are tracked. Whether the driver is awake or asleep is identified by matching the extracted eye image with the externally fed template (open eyes and closed eyes) based on eyes opening and eyes closing, blinking is recognized. If the driver falling asleep state remains above a specific time (the threshold time) the vehicles stops and an alarm is activated by the use of a specific microcontroller, in this prototype an Arduino is used.

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

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