scholarly journals Smart Security System for Theft Protection Using Face Recognition

2021 ◽  
pp. 310-313
Author(s):  
Snehal Bhowate ◽  
Kanchan Bashine ◽  
Poonam Gajbhiye ◽  
Samiksha Paidlewar ◽  
Nikhil Dharpure ◽  
...  
Keyword(s):  
Real Time ◽  
Euclidean Distance ◽  
User Authentication ◽  
Threshold Value ◽  
Safety System ◽  
Vehicle Safety ◽  
Linear Discriminant ◽  
Parking Lot ◽  
The Face ◽  
Face Detection And Recognition

The number of car robbery attempts at the local and international scale is rising rapidly in this modern era. By inventing robbery techniques, the owners are afraid that their cars will be robbed from their ordinary parking lot or from outside. This makes vehicle protection against robbery important as a result of insecurity. The computer vision based real-time vehicle safety system solves this problem. The proposed car safety system carries through real time user authentication based on image processing using face detection and recognition techniques and a microprocessor-based control system attached to the car. The infrarot sensor attached to the driver's vehicles seat activates the hidden camera, which is fixed inside the vehicle, as the person enters the parked vehicle overcoming the existing security features. The person's face is detected using Viola Jones algorithm once the image is obtained from the activated camera. The extracted face is recognized using the improved Linéar Discriminant Analysis (LDA) algorithm that distinguishes many features rather than looking for an exact pattern based on the Euclidean distance. Authorization requires that the threshold value is established and compared to the Euclidean distance over which the person is not authenticated. The face is sent to the mobile of the owner as an MMS via the operating GSM modem, which is classified as unknown. The owner shall be controlled with the relay in accordance with the owner’s command when the information is received. The way to authenticate the person would be efficient and efficient in terms of vehicle safety.

A real-time vehicle safety system

2012 ◽  
Author(s):  
Yongquan Chen ◽  
Yuandong Sun ◽  
Ning Ding ◽  
Wing Kwong Chung ◽  
Huihuan Qian ◽  
...  
Keyword(s):  
Real Time ◽  
Safety System ◽  
Vehicle Safety

scholarly journals Design and Analysis of IOT Based Real Time System for Door Locking/Unlocking using Face Identification

2020 ◽  
Vol 8 (5) ◽  
pp. 2093-2095
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Raspberry Pi ◽  
Real Time System ◽  
Security Issues ◽  
Door Opening ◽  
The Face ◽  
Face Detection And Recognition ◽  
Real Time Application ◽  
Detection And Recognition

In this era we are facing security issues in every aspect. So for resolving this issue we are proposes a real time application controlled door locking/unlocking mechanism which harnesses the power of IOT and machine learning for smooth functionality. The door unlocking system proposed here uses a Raspberry Pi 3 model B for computation along with a Pi Camera to take face as an input of the user. Also in order to make door unlocking fail proof, fingerprint sensor is used. Scenarios like bad lighting and camera failure can be easily dealt using this sensor. The face detection and recognition system used for door opening will be able to learn user’s faces from time to time and update its dataset. So any subtle changes in the face of user like addition of spectacles or removal of beard can be easily dealt with.

Comparison of Face Detection and Recognition Algorithms in Real-Time Video

2020 ◽  
Author(s):  
Alejandra Sarahi Sanchez-Moreno ◽  
Hector Manuel Perez-Meana ◽  
Jesus Olivares-Mercado ◽  
Gabriel Sanchez-Perez ◽  
Karina Toscano-Medina
Keyword(s):  
Real Time ◽  
Face Detection ◽  
High Performance ◽  
Facial Recognition ◽  
Rapid Development ◽  
Real Time Systems ◽  
The Face ◽  
Face Detection And Recognition ◽  
Time Systems ◽  
Detection And Recognition

Facial recognition systems has captivated research attention in recent years. Facial recognition technology is often required in real-time systems. With the rapid development, diverse algorithms of machine learning for detection and facial recognition have been proposed to address the challenges existing. In the present paper we proposed a system for facial detection and recognition under unconstrained conditions in video sequences. We analyze learning based and hand-crafted feature extraction approaches that have demonstrated high performance in task of facial recognition. In the proposed system, we compare different traditional algorithms with the avant-garde algorithms of facial recognition based on approaches discussed. The experiments on unconstrained datasets to study the face detection and face recognition show that learning based algorithms achieves a remarkable performance to face the challenges in real-time systems.

scholarly journals Software Development Framework for Real-Time Face Detection and Recognition in Mobile Devices

2020 ◽  
Vol 14 (04) ◽  
pp. 103
Author(s):  
Laxmisha Rai ◽  
Zhiyuan Wang ◽  
Amila Rodrigo ◽  
Zhaopeng Deng ◽  
Haiqing Liu
Keyword(s):  
Face Recognition ◽  
Software Development ◽  
Real Time ◽  
Mobile Devices ◽  
Face Detection ◽  
Scale Transformation ◽  
Development Framework ◽  
The Face ◽  
Face Detection And Recognition ◽  
Detection And Recognition

With the rapid use of Android OS in mobile devices and related products, face recognition technology is an essential feature, so that mobile devices have a strong personal identity authentication. In this paper, we propose Android based software development framework for real-time face detection and recognition using OpenCV library, which is applicable in several mobile applications. Initially, the Gaussian smoothing and gray-scale transformation algorithm is applied to preprocess the source image. Then, the Haar-like feature matching method is used to describe the characteristics of the operator and obtain the face characteristic value. Finally, the normalization method is used to match the recognition of face database. To achieve the face recognition in the Android platform, JNI (Java Native Interface) is used to call the local Open CV. The proposed system is tested in real-time in two different brands of smart phones, and results average success rate in both devices for face detection and recognition is 95% and 80% respectively.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

scholarly journals Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 265
Author(s):  
Ruchi Sharma ◽  
Wenzhe Zang ◽  
Menglian Zhou ◽  
Nicole Schafer ◽  
Lesa A. Begley ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Real Time ◽  
Rapid Assessment ◽  
Principal Component ◽  
Corticosteroid Treatment ◽  
Breath Analysis ◽  
Blood Eosinophil ◽  
Exhaled Breath ◽  
Respiratory Illnesses ◽  
Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

scholarly journals An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

2021 ◽  
Vol 13 (12) ◽  
pp. 2259
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Qing Zhao ◽  
Zihan Peng ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Threshold Value ◽  
Deformation Monitoring ◽  
Success Rates ◽  
Multipath Mitigation ◽  
Mode Decomposition ◽  
Observation Noise ◽  
Original Observation ◽  
Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

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