scholarly journals Exploration of Face Detection Methods in Digital Images

2019 ◽  
Vol 8 (4) ◽  
pp. 12130-12136
Keyword(s):  
Face Detection ◽  
Digital Images ◽  
Recognition System ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Human Beings ◽  
Survey Paper ◽  
Wide Range ◽  
The Face

Face detection is a challenging computer vision task that identifies and localizes the faces of human beings from digital images or video streams. It is predominantly the first phase in the process of developing a wide range of face applications such as face recognition, emotion recognition, authentication, surveillance systems etc. The process of face detection is easy from the human perspective but, a complex task for computers that involves searching of the face in variable circumstances of pose, colour, size, occlusion, illumination etc. If the outcome of face detection is intended to be input for another algorithm, an accurate, well informed selection of an appropriate face detection technique is essential because the overall performance of face application is dependent on face detection algorithm’s precision. The survey paper presents a review of three commonly used face detection algorithms available in literature namely Viola Jones, Neural networks (NN) and Local Binary Pattern (LBP) for the purpose of ascertaining the most suitable face detection algorithm to implement for our future work in developing an ‘Online student concentration level recognition system’.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

Based on the Adaboost Algorithm of Face Detection Research

2014 ◽  
Vol 635-637 ◽  
pp. 985-988
Author(s):  
Wei Bo Yu ◽  
Lin Zhao ◽  
Wei Ming He
Keyword(s):  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Adaboost Algorithm ◽  
Complex Background ◽  
Complex Image ◽  
Face Recognition System ◽  
Lower Accuracy ◽  
The Face

Because of the influence of complex image background, illumination changes, facial rotation and some other factors, makes face detection in complex background is much more difficult, lower accuracy and slower speed. Adaboost algorithm was used for face detection, and implemented the test process in OpenCV. Face detection experiments were performed on images with facial rotation and complex background, the detection accuracy rate was 85% and 99% respectively, the average detection time of each picture was 16.67ms and 76ms.Experimental results show that the face detection algorithm can accurately and quickly realize face detection in complex background, and can satisfy the requirements of real-time face recognition system.

Research on Fast Face Detection Algorithm in Complex Background

2014 ◽  
Vol 1030-1032 ◽  
pp. 1779-1782
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Prior Knowledge ◽  
Face Detection ◽  
Fast Algorithm ◽  
Vertical Direction ◽  
Detection Algorithm ◽  
Color Information ◽  
Human Eye ◽  
Face Region ◽  
Complex Background ◽  
The Face

This paper presents a fast algorithm for face detection in complex background, in which image color information is used first, project upper part of the partition of face in the gray image to horizontal and vertical direction. Determine the eyes positions by the minimum value, and scope the human eye in the face of prior knowledge to judge and adjust the face region.

Efficient Half Face Detection System Based on Linear Binary Pattern

2020 ◽  
Vol 17 (5) ◽  
pp. 2342-2348
Author(s):  
Ashutosh Upadhyay ◽  
S. Vijayalakshmi
Keyword(s):  
Face Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Data Set ◽  
Computation Cost ◽  
Face Features ◽  
Positive Rate ◽  
The Face

In the field of computer vision, face detection algorithms achieved accuracy to a great extent, but for the real time applications it remains a challenge to maintain the balance between the accuracy and efficiency i.e., to gain accuracy computational cost also increases to deal with the large data sets. This paper, propose half face detection algorithm to address the efficiency of the face detection algorithm. The full face detection algorithm consider complete face data set for training which incur more computation cost. To reduce the computation cost, proposed model captures the features of the half of the face by assuming that the human face is symmetric about the vertical axis passing through the nose and train the system using reduced half face features. The proposed algorithm extracts Linear Binary Pattern (LBP) features and train model using adaboost classifier. Algorithm performance is presented in terms of the accuracy i.e., True Positive Rate (TPR), False Positive Rate (FTR) and face recognition time complexity.

A Face Detection Algorithm in Color Image Based on Skin Color Segmentation

2013 ◽  
Vol 811 ◽  
pp. 417-421
Author(s):  
Shi Lei
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Detection Algorithm ◽  
Color Segmentation ◽  
Skin Region ◽  
Otsu Method ◽  
The Face ◽  
Segmentation Image ◽  
Ellipse Area

Aiming at color images under complex background, this paper put forward a face detection algorithm based on skin color segmentation, combining the geometric characteristics. The skin region can be obtained by using skin color model and OTSU method to automatically optimize threshold segmentation image. By analyzing the characteristics of skin color region, the face position is determined by criterion of ellipse area.

scholarly journals Mask Detection and Tracing System

2021 ◽  
pp. 406-412
Author(s):  
Sanket Shete ◽  
Kiran Tingre ◽  
Ajay Panchal ◽  
Vaibhav Tapse ◽  
Prof. Bhagyashri Vyas
Keyword(s):  
Neural Networks ◽  
Face Detection ◽  
Large Scale ◽  
Face Mask ◽  
Detection Algorithm ◽  
Detection Task ◽  
Loss Of Face ◽  
Satisfactory Performance ◽  
Training Samples ◽  
The Face

Covid19 has given a new identity for wearing a mask. It is meaningful when these masked faces are detected accurately and efficiently. As a unique face detection task, face mask detection is much more difficult because of extreme occlusions which leads to the loss of face details. Besides, there is almost no existing large-scale accurately labelled masked face dataset, which increase the difficulty of face mask detection. The system encourages to use CNN-based deep learning algorithms which has done vast progress towards researches in face detection In this paper, we propose novel CNN-based method which is formed of three convolutional neural networks to detect face mask. Besides, because of the shortage of face masked training samples, we propose a new dataset called” face mask dataset” to finetune our CNN models. We evaluate our proposed face mask detection algorithm on the face mask testing set, and it achieves satisfactory performance

scholarly journals Face Recognition Door Lock System using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 138-145
Author(s):  
K. V. Usha Ramani
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Initial Step ◽  
Power Input ◽  
Human Interaction ◽  
Raspberry Pi ◽  
Identification System ◽  
Human Beings ◽  
The Face ◽  
Door Lock

One of the crucial difficulties we aim to find in computer vision is to recognize items automatically without human interaction in a picture. Face detection may be seen as an issue when the face of human beings is detected in a picture. The initial step towards many face-related technologies, including face recognition or verification, is generally facial detection. Face detection however may be quite beneficial. A biometric identification system besides fingerprint and iris would likely be the most effective use of face recognition. The door lock system in this project consists of Raspberry Pi, camera module, relay module, power input and output, connected to a solenoid lock. It employs the two different facial recognition algorithms to detect the faces and train the model for recognition purpose

scholarly journals Face recognition attendance system using Local Binary Pattern (LBP)

2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Automated System ◽  
Student Attendance ◽  
Recognition Method ◽  
Face Recognition System ◽  
The Face

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.

scholarly journals The importance of age-specific data in routine syndromic surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Roger Morbey ◽  
Alex J. Elliot ◽  
Gillian E. Smith
Keyword(s):  
Public Health ◽  
Hospital Admissions ◽  
Age Distribution ◽  
Age Groups ◽  
Health Indicators ◽  
Older Age ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Wide Range

ObjectiveTo investigate whether aberration detection methods for syndromicsurveillance would be more useful if data were stratified by age band.IntroductionWhen monitoring public health incidents using syndromicsurveillance systems, Public Health England (PHE) uses the ageof the presenting patient as a key indicator to further assess theseverity, impact of the incident, and to provide intelligence on thelikely cause. However the age distribution of cases is usually notconsidered until after unusual activity has been identified in the all-ages population data. We assessed whether monitoring specific agegroups contemporaneously could improve the timeliness, specificityand sensitivity of public health surveillance.MethodsFirst, we examined a wide range of health indicators from the PHEsyndromic surveillance systems to identify for further study thosewith the greatest seasonal variation in the age distribution of cases.Secondly, we examined the identified indicators to ascertain whetherany age bands consistently lagged behind other age bands. Finally,we applied outbreak detection methods retrospectively to age specificdata, identifying periods of increased activity that were only detectedor detected earlier when age-specific surveillance was used.ResultsSeasonal increases in respiratory indicators occurred first inyounger age groups, with increases in children under 5 providingearly warning of subsequent increases occurring in older age groups.Also, we found age specific indicators improved the specificity ofsurveillance using indicators relating to respiratory and eye problems;identifying unusual activity that was less apparent in the all-agespopulation.ConclusionsRoutine surveillance of respiratory indicators in young childrenwould have provided early warning of increases in older age groups,where the burden on health care usage, e.g. hospital admissions, isgreatest. Furthermore this cross-correlation between ages occurredconsistently even though the age distribution of the burden ofrespiratory cases varied between seasons. Age specific surveillancecan improve sensitivity of outbreak detection although all-agesurveillance remains more powerful when case numbers are low.

scholarly journals Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Visually Impaired ◽  
Recognition System ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
The Face ◽  
Visually Impaired Students ◽  
Facial Recognition System

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.

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