Face recognition and tracking for security surveillance

2021 ◽  
pp. 1-9
Author(s):  
Sreelu P. Nair ◽  
K. Abhinav Reddy ◽  
Prithvi Krishna Alluri ◽  
S. Lalitha
Keyword(s):  
Face Recognition ◽  
Efficient Solution ◽  
Camera Network ◽  
Mass Gatherings ◽  
Missing Children ◽  
Mobile Cameras ◽  
Security Surveillance ◽  
The City

According to the National Crime Records Bureau, 63,407 children have gone missing in the year 2016, which makes almost 174 children go missing in India every day, out of which only 50% are ever found again. This brings up a need for an efficient solution to trace missing children. The proposed solution uses machine assistance during these search activities with face recognition technologies and can be used for essential development of applications which use CCTV footage across a camera network to identify the person lost. In our solution we use One Shot learning for face recognition to identify stranded people in places such as mass gatherings. The same technology can be used for identification of criminals across the city. The paper also talks about the tracking of people across a network of multiple non-overlapping cameras, with a feature of shifting the target tovehicle, if the target gets into one. The experimentation is performed using mobile cameras and thus, helps in monitoring actions of criminals and finding their hideout.

scholarly journals InnovFaceNet: Deep Face Recognition for Industrial Environments

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Nagarjun Gururaj ◽  
Kanika Batra
Keyword(s):  
Face Recognition ◽  
Criminal Justice ◽  
Real Time ◽  
Intelligent Systems ◽  
Facial Recognition ◽  
Industrial Plant ◽  
The World ◽  
Industrial Environments ◽  
Or Organization ◽  
Security Surveillance

In recent times the usage of intelligent systems have paved way formany applications to be robust and self-reliant. One such popularand vast growing technology is face recognition. Facial Recognitiontechnology is used in security, surveillance, criminal justice systemsand many other multimedia platforms. This work proposes a realtime facial recognition technology which can be used in any industrialsetup eliminating manual supervision, ensuring authorized accessto the personnel in the plant. Due to the recent development ofCOVID-19 pandemic around the world, wearing masks has becomea necessity. Our proposed facial recognition technology identifies aperson’s face with mask or no mask in real time with a speed of20 FPS on a CPU and an F1-score of 95.07%. This makes ouralgorithm fast, secure, robust and deployable on a simple personalcomputer or any edge device at any industrial plant or organization.

scholarly journals Surveillance using Face Recognition in Smart Cities

2020 ◽  
Vol 9 (4) ◽  
pp. 2042-2047
Keyword(s):  
Face Recognition ◽  
Video Surveillance ◽  
Crime Rate ◽  
Smart Cities ◽  
Police Department ◽  
Indian Government ◽  
The Past ◽  
Time To Build ◽  
Voting System ◽  
The City

In this project safe city demonstrates how the security in India can be increased with the help of video surveillance using facial recognition. In the Aadhar Card database, the Indian Government has stored fingerprint and Iris details of every civilian in India. But the Indian Government is only using the Fingerprint details in the voting system to avoid fake votes. With the help of this project any person roaming in the city limit can be easily monitored. This will be a very useful technology for the Police Department of India to track the criminals and to reduce crime rate. Whenever a person or criminal is needed to be traced , the photo of the target is uploaded into the software. The uploaded photo will be cross-checked by the software with the videos captured from the surveillance cameras. It will then identify the person based on the percentage of accuracy to be matched. In the past 5 years Indian Government have made many cities into smart cities. But now it’s time to build safe cities for India.

Mahalanobis Distance for Face Recognition

2015 ◽  
pp. 31-38
Keyword(s):  
Face Recognition ◽  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
City Block ◽  
Underlying Distribution ◽  
Block Based ◽  
The City

If two vectors originate from the same underlying distribution, the distance between them could be computed with the Mahalanobis distance, a generalization of the Euclidean one. Also, it can be defined as the Euclidean distance computed in the Mahalanobis space. Moreover, there exist also the city block-based Mahalanobis distance and other versions including the angle- and cosine-based ones. Largely employed for face recognition with bi-dimensional facial data, Mahalanobis gains very good performances with PCA algorithms.

scholarly journals Implementation of Aadhaar Verified, face Recognition based Security Surveillance

2020 ◽  
Vol 9 (9) ◽  
pp. 415-420
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Human Face ◽  
Element Determination ◽  
Python Language ◽  
Cascade Calculation ◽  
Haar Cascade ◽  
Two Phases ◽  
Security Surveillance ◽  
Human Faces

Security and Authentication is a basic piece of any industry. In Real time, Human face acknowledgment can be acted in two phases, for example, Face discovery and Face acknowledgment. This paper actualizes "Haar-Cascade calculation" to distinguish human faces which are sorted out in Open CV by Python language. Gathering with other existing calculations, this classifier creates a high acknowledgment rate even with shifting articulations, effective element determination and low combination of bogus positive highlights. Haar highlight based course classifier framework uses just 200 highlights out of 6000 highlights to yield an acknowledgment pace of 85-95%.

scholarly journals Powerful processing to three-dimensional facial recognition using triple information

2020 ◽  
Vol 9 (4) ◽  
pp. 326
Author(s):  
Mohammad Karimi Moridani ◽  
Ahad Karimi Moridani ◽  
Mahin Gholipour
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Principal Component ◽  
Support Vector ◽  
The Face ◽  
Principal Component Analysis Algorithm ◽  
The Difference ◽  
Security Surveillance ◽  
The Trinity

<p><span>Face Detection plays a crucial role in identifying individuals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face. The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing.</span></p>

scholarly journals Issues of the Legal Regulation of the Procedure for Bringing to Administrative Liability for the Violation of the High-Alert Mode in Conditions of the Pandemic of the New Coronavirus Infection (COVID-19)

2020 ◽  
Vol 12 ◽  
pp. 32-35
Author(s):  
Mikhail I. Nikulin ◽  
◽  
Lada A. Rerikh ◽  
Keyword(s):  
Face Recognition ◽  
Surveillance System ◽  
Legal Regulation ◽  
Video Surveillance System ◽  
Constitutional Rights ◽  
Coronavirus Infection ◽  
The Russian Federation ◽  
Federal Laws ◽  
The City ◽  
Administrative Responsibility

The article analyzes the procedure for imposing to administrative responsibility under article 3.18.1 of the Code of Administrative offenses of Moscow for violation of the high-alert regime. The article deals with problematic issues of compliance with the constitutional rights of citizens who are imposed to administrative responsibility in the case of automated recording of an offense using the city video surveillance system and face recognition technologies It is made a reasoned conclusion about inadmissibility of objective accusation and illegality of distribution of the order of attraction to administrative responsibility, established in part 3 of article 28.6 of the Code of Administrative offenses of the Russian Federation, the offense that are not provided by federal laws.

Face Recognition in Poor-Quality Video: Evidence From Security Surveillance

1999 ◽  
Vol 10 (3) ◽  
pp. 243-248 ◽  
Author(s):  
A. Mike Burton ◽  
Stephen Wilson ◽  
Michelle Cowan ◽  
Vicki Bruce
Keyword(s):  
Face Recognition ◽  
Poor Quality ◽  
Quality Video ◽  
Security Surveillance
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