An automated criminal identification system is proposed that uses face detection and recognition in real-time video streams from surveillance cameras. The system contains three databases: a citizen database with citizen facial images and IDs, a local watch list database with criminal facial images and details, and an international watch list database for non-citizen criminals. When a face is detected, it is compared to the citizen database, and if no match, compared to the watch lists. If a match is found to a watch list entry, police are notified. This system aims to help identify criminals and unknown individuals caught on camera.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Human face detection and recognition is an important technology used in various applications such as video monitor system. Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of that systems like automatic attendance is used. To overcome the problems like wastage of time, incorrect attendance, the proposed system gives a method like when he enters the class room , system marks the attendance by extracting the image using Principal Component Analysis algorithm. The system will record the attendance of the student automatically. The student database is collected, it includes name of the students, there images and roll number. It carries an entry in log report of every student of each subject and generates a pdf report of the attendance of the student.
Face detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)SSII
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)
6/10 (木) 14:30~15:00
講師:Huy H. Nguyen 氏(総合研究大学院大学/国立情報学研究所)
概要: Advances in machine learning and their interference with computer graphics allow us to easily generate high-quality images and videos. State-of-the-art manipulation methods enable the real-time manipulation of videos obtained from social networks. It is also possible to generate videos from a single portrait image. By combining these methods with speech synthesis, attackers can create a realistic video of some person saying something that they never said and distribute it on the internet. This results in loosing social trust, making confusion, and harming people’s reputation. Several countermeasures have been proposed to tackle this problem, from using hand-crafted features to using convolutional neural network. Some countermeasures use images as input and other leverage temporal information in videos. Their output could be binary (bona fide or fake) or muti-class (deepfake detection), or segmentation masks (manipulation localization). Since deepfake methods evolve rapidly, dealing with unseen ones is still a challenging problem. Some solutions have been proposed, however, this problem is not completely solved. In this talk, I will provide an overview on both deepfake generation and deepfake detection/localization. I will mainly focus on image and video domain and also introduce some audiovisual-based methods on both sides. Some open discussions and future directions are also included.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
"Attendance Management System bridges the effective communication between students, teachers, and parents by keep them notified about their wards' attendance via Email or SMS.
A.T.S.I. offers best biometric attendance management system, face recognition attendance system, fingerprint based attendance system, and RFID based attendance system and gives flexibility to institutions to choose the suitable system for them."
The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database
Human face detection and recognition is an important technology used in various applications such as video monitor system. Traditional method for taking attendance is Roll Number of student and record the attendance in sheet which takes a lot of time. Because of that systems like automatic attendance is used. To overcome the problems like wastage of time, incorrect attendance, the proposed system gives a method like when he enters the class room , system marks the attendance by extracting the image using Principal Component Analysis algorithm. The system will record the attendance of the student automatically. The student database is collected, it includes name of the students, there images and roll number. It carries an entry in log report of every student of each subject and generates a pdf report of the attendance of the student.
Face detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)SSII
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)
6/10 (木) 14:30~15:00
講師:Huy H. Nguyen 氏(総合研究大学院大学/国立情報学研究所)
概要: Advances in machine learning and their interference with computer graphics allow us to easily generate high-quality images and videos. State-of-the-art manipulation methods enable the real-time manipulation of videos obtained from social networks. It is also possible to generate videos from a single portrait image. By combining these methods with speech synthesis, attackers can create a realistic video of some person saying something that they never said and distribute it on the internet. This results in loosing social trust, making confusion, and harming people’s reputation. Several countermeasures have been proposed to tackle this problem, from using hand-crafted features to using convolutional neural network. Some countermeasures use images as input and other leverage temporal information in videos. Their output could be binary (bona fide or fake) or muti-class (deepfake detection), or segmentation masks (manipulation localization). Since deepfake methods evolve rapidly, dealing with unseen ones is still a challenging problem. Some solutions have been proposed, however, this problem is not completely solved. In this talk, I will provide an overview on both deepfake generation and deepfake detection/localization. I will mainly focus on image and video domain and also introduce some audiovisual-based methods on both sides. Some open discussions and future directions are also included.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
"Attendance Management System bridges the effective communication between students, teachers, and parents by keep them notified about their wards' attendance via Email or SMS.
A.T.S.I. offers best biometric attendance management system, face recognition attendance system, fingerprint based attendance system, and RFID based attendance system and gives flexibility to institutions to choose the suitable system for them."
The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database
CRIMINAL IDENTIFICATION FOR LOW RESOLUTION SURVEILLANCEvivatechijri
Criminal Identification System allows the user to identify a certain criminal based on their biometrics. With advancements in security technology, CCTV cameras have been installed in many public and private areas to provide surveillance activities. The CCTV footage becomes crucial for understanding of the criminal activities that take place and to detect suspects. Additionallywhen a criminal is found it is difficult to locate and track him with just his image if he is on the run. Currently this procedure consists of finding such people in CCTV surveillance footage manually which is time consuming. It is also a tedious process as the resolution for such CCTV cameras is quite low. As a solution to these issues, the proposed system is developed to go through real time surveillance footage, detect and recognize the criminals based on reference datasets of criminals. The use of facial recognition for identifying criminals proves to bebeneficial. Once the best match is found the real time cropped image of the recognized criminal is saved which can be accessed by authorized officials for locating and tracking criminals or for further investigative use.
Physical and Cyber Crime Detection using Digital Forensic Approach: A Complet...IJARIIT
Criminalization may be a general development that has significantly extended in previous few years. In
order, to create the activity of the work businesses easy, use of technology is important. Crime investigation analysis
is a section records in data mining plays a crucial role in terms of predicting and learning the criminals. In our
paper, we've got planned an incorporated version for physical crime as well as cybercrime analysis. Our approach
uses data mining techniques for crime detection and criminal identity for physical crimes and digitized forensic tools
(DFT) for evaluating cybercrimes. The presented tool named as Comparative Digital Forensic Process tool
(CDFPT) is entirely based on digital forensic model and its stages named as Comparative Digital Forensic Process
Model (CDFPM). The primary step includes accepting the case details, categorizing the crime case as physical crime
or cybercrime and sooner or later storing the data in particular databases. For physical crime analysis we've used kmeans
approach cluster set of rules to make crime clusters. The k-means method effects are a lot advantageous by the
utilization of GMAPI generation. This provides advanced and consumer-friendly visual-aid to k-means approach for
tracing the region of the crime. we have applied KNN for criminal identification with the
help of observing beyond crimes and finding similar ones that suit this crime, if no past document is discovered then
the new crime sample are introduced to the crime data-set. With the advancements of web, the network form has
become much more complicated and attacking methods are further more than that as well. For crime analysis
we're detecting the attacks executed on host system through an outsider the usage of
assorted digitized forensic tools to produce information security with the help of generating reports for an
event which could need any investigation. Our digitized technique aids the development of the society
by helping the investigation businesses to follow a custom-built investigative technique in crime analysis and criminal
identification as opposed to manually looking the database to analyze criminal activities, and as a
result facilitate them in combating crimes.
Digital Ethics for Biometric Applications in a Smart CityAraz Taeihagh
From border control using fingerprints to law enforcement with video surveillance to self-activating devices via voice identification, biometric data is used in many applications in the contemporary context of a Smart City. Biometric data consists of human characteristics that can identify one person from others. Given the advent of big data and the ability to collect large amounts of data about people, data sources ranging from fingerprints to typing patterns can build an identifying profile of a person. In this article, we examine different types of biometric data used in a smart city based on a framework that differentiates between profile initialization and identification processes. Then, we discuss digital ethics within the usage of biometric data along the lines of data permissibility and renewability. Finally, we provide suggestions for improving biometric data collection and processing in the modern smart city.
The foundations for biometrics or identification systems were laid long ago. Today these developments have contributed to the identification of people, access to private sites and all places that need security and order with the help of computerized computers that perform biometric facial recognition, exclusively based on images of human faces for their function. With the extraction of facial midst characteristics of each person provides information used for the detection of the face. This communication also addresses the different processes, stages and methods of feature extraction operated by facial recognition systems. Including the positive and negative aspects of the implementation of these, the advantages and disadvantages, peoples criteria in this respect. Tovbaev Sirojiddin | Karshiboev Nizomiddin "Image Based Facial Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31330.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31330/image-based-facial-recognition/tovbaev-sirojiddin
10 Criminology in the FutureCriminology in the FutureKristop.docxhyacinthshackley2629
10 Criminology in the Future
Criminology in the Future
Kristopher Freitag, Javielle Watson, Michael Westphal, Starcia Zeigler
CJA/314
April 7, 2014
Judy Mazzucca
Technology is advancing in every aspect of the criminal justice system, from the investigation to the prosecution of the crimes. Crime fighting methodologies have the potential to greatly assist law enforcement in the war on crime. Some experts even think that some software and tools will be able to help prevent crime. (Yeung, n.d.). Methodologies, such as mandating DNA collection programs, biometrics, and implementing cybercrime spyware programs are on the list of the next big things of the future, when it comes to fighting crime. DNA testing helps law enforcement investigate and prosecute crimes, as well as clear the names of those who have been wrongfully convicted. There are currently about twenty states with laws requiring DNA collection at the time of the person’s arrest. The federal government also has this requirement. As, with any controversial subject, DNA testing has its critics. Some are saying that DNA testing is in violation of the Fourth Amendment, especially for those who have not been convicted of a crime. Others are concerned that DNA testing may open the doors for abuse of the genetic information being stored in the databases. (Berson, n.d.). Biometrics are automated methods of recognizing a person based on physiological or behavioral characteristics. Some of the features measured using biometrics are handwriting, voice, iris, hand geometry, vein, retinal, and fingerprints. Biometric based solutions provide personal data privacy, and confidential financial transactions, and are starting to become the foundation of an extensive array of highly secure identification and personal verification solutions. The need for highly secure identification and personal verification technologies is great, due to the increased number of transaction fraud and security breaches. This need is especially great in the areas of local, state, and federal governments. Infrastructures such as electronic banking, health and social services, law enforcement, and retail sales are already taking advantage of, and seeing the benefits of biometric technology. ("The Biometrics Consortium", n.d.).
As we become more and more dependent on technology, the increase of cybercrimes are skyrocketing, which has forced law enforcement to figure out ways of combatting cybercrimes. We have become extremely vulnerable to many cybercrimes, including social media fraud, which consists of cyber criminals using social media to steal the identities of unsuspecting people; and luring people to download malicious materials, or reveal their passwords; corporate security breaches, which consists of cyber criminals exploiting company employees via scams; and phishing, which involves cyber criminals targeting company employees by sending emails that appear to be from someone within the company. ("Homeland .
Crime analysis mapping, intrusion detection using data miningVenkat Projects
Crime analysis mapping, intrusion detection using data mining
Data Mining plays a key role in Crime Analysis. There are many different algorithms mentioned in previous research papers, among them are the virtual identifier, pruning strategy, support vector machines, and apriori algorithms. VID is to find relation between record and vid. The apriori algorithm helps the fuzzy association rules algorithm and it takes around six hundred seconds to detect a mail bomb attack. In this research paper, we identified Crime mapping analysis based on KNN (K – Nearest Neighbor) and ANN (Artificial Neural Network) algorithms to simplify this process. Crime Mapping is conducted and Funded by the Office of Community Oriented Policing Services (COPS). Evidence based research helps in analyzing the crimes. We calculate the crime rate based on the previous data using data mining techniques. Crime Analysis uses quantitative and qualitative data in combination with analytic techniques in resolving the cases. For public safety purposes, the crime mapping is an essential research area to concentrate on. We can identity the most frequently crime occurring zones with the help of data mining techniques. In Crime Analysis Mapping, we follow the following steps in order to reduce the crime rate: 1) Collect crime data 2) Group data 3) Clustering 4) Forecasting the data. Crime Analysis with crime mapping helps in understanding the concepts and practice of Crime Analysis in assisting police and helps in reduction and prevention of crimes and crime disorders.
Running Head CRIMINOLOGY USE OF COMPUTER APPLICATIONS .docxtodd271
Running Head: CRIMINOLOGY USE OF COMPUTER APPLICATIONS 2
CRIMINOLOGY USE OF COMPUTER APPLICATIONS 2
In the wake of technological advances, the use of computers has played a major role especially in criminal justice (Moriarty, (2017). This paper has focused on the use of computer application technologies in criminology and the potential it has in legal systems. From enabling easy access for witnesses to search for accused peoples’ photographs on the screen and go through the whole court procedural activities. Moreover, criminals’ records can be monitored using databases and it is easy to make a follow-up on crimes they have committed in the past and the charges against them. Forensics can also be conducted and investigations can now be carried out easily and very fast. Also, when one is linked to cases, they can be easily identified using forensics and fingerprints. Portable laptops have also helped police officers in getting information and any important details related to a crime at any place without having to go back to their working stations. James (2017), argues that unlike in the past, investigations are done faster due to internet connections and ease of communication between community members and investigative officers through the use of phone gadgets.
Computers have broad variance in usage which has been enhanced by computer applications. For instance, massive record keeping systems have relied for reference on criminal accounts, case records and unresolved warranties. Incorporation of technology in criminology has just made the career easy and also improved livelihoods. Many police units now use computerized applications to keep up with the ever-rising crimes. There are different applications being used nowadays, from mobile technology, to use in-car computers, CCTV camera installations and also software such as the Computer Aided Dispatch. Investigators often use programmed record management systems to monitor information they obtain and guard it properly. With the current technology, it is possible to detect impending crimes, track stolen goods and the culprits, tell which time a crime occurred and also who committed it and where.
Computer applications:
1. In-Car Computer installations in police cars.
Blumstein (2018), contends that this application that allows traffic patrol police to effectively carry out their activities especially when vehicles violate traffic rules. In the current world, things are drifting toward being more computerized than handwritten (Maxfield & Babbie, 2014). Thus event arrest reports are being typed. It also means that after traffic references are written down, they are generated by the computers installed duplicating a copy to the person who breaks the rules. This is seen to reduce paperwork and improve the efficiency of police officers' work.
2. Computer Aided Dispatch
In the past, correspondents would use hand.
Comparative Analysis of Face Recognition Methodologies and TechniquesFarwa Ansari
In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
approaches are basically used for face recognition
methodologies. Eigen Faces, Fisher Faces and LBP
methodologies were introduced for recognition purpose.
Eigen Faces is most frequently used because of its
efficiencies. To observe the efficient techniques of facial
recognition, there are many scenarios to measure its
performance which are based on real time.
Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalR - Slides Onl...Peter Gallagher
In this session delivered at Leeds IoT, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub
MATHEMATICS BRIDGE COURSE (TEN DAYS PLANNER) (FOR CLASS XI STUDENTS GOING TO ...PinkySharma900491
Class khatm kaam kaam karne kk kabhi uske kk innings evening karni nnod ennu Tak add djdhejs a Nissan s isme sniff kaam GCC bagg GB g ghan HD smart karmathtaa Niven ken many bhej kaam karne Nissan kaam kaam Karo kaam lal mam cell pal xoxo
3. ABSTRACT
As the world has seen exponential advancement over the last decade, there is an
abnormal increase in the crime rate and also the number of criminals are
increasing at an alarming rate, this leads toward a great concern about the
security issues. various causes of theft, stealing crimes, burglary, kidnapping,
human trafficking etc. are left unsolved because the availability of police
personnel is limited, many times there is no identification of the person who was
involved in criminal activities. To avoid this situation an automated facial
recognition system for criminal identification is proposed using Haar feature-
based cascade classifier. This paper presents a real-time face recognition using an
automated surveillance camera. This system will be able to detect and recognize
face automatically in real-time.
4. INTRODUCTION
• The face is crucial for human identity. It is the feature which best distinguishes a person. Face detection and
recognition is the technology which is used to identify a person from a video or image.
• we propose a face detection and recognition system for criminal identification using python along with
OpenCV package.
• Most of the common facial recognition techniques include target matching method, geometric feature
recognition method, and principal component analysis method and so on.
• Most of the criminals are mingled with us in our society and they are much hard to identify.
• Traditionally, repeated criminals are identified by their biometrics such as thumbprint. But criminals are
smart enough to not to leave their biometrics in crime scene.
• In developed countries, the government create dataset which is helpful for recognize the human face which
compares the suspicious act with trained dataset and information stored in database.
• Face recognition system built by using Principal Component Analysis (PCA) method. The two main
disadvantages of using the PCA method are that computational complexity is high and it can only process
the faces that have similar facial expressions.
• This technology is a widely used biometrics system for authentication, authorization, verification and
identification.
• Applying principal component analysis for finding distinguishable features from many images to get the
similarity for the target image.
5. LITERATURE SURVEY
SL.NO YEAR AUTHOR TITLE METHODOLOGY
1. 2017 Nurul Azma
Abdullah, Md.
Jamri Saidi and
Nurul Hidayah Ab
Rahman “
Face recognition for criminal
identification.
An implementation of principal
component analysis for face
recognition.
2. 2019 Apoorva.P,
Ramesh.B and
Varshitha.M.R “
Automatic face recognition. Automated criminal identification
by face recognition using open
computer vision classifiers.
3. 2018 Rasanayagam,
K.Kumarasiri,
S.D.D, Tharuka,
W. A. D.
Samaranayake, N.
Samarasinghe
and P.
Siriwardana
CIS : An Automated Criminal
Identification System.
Recognition with deep learning
approach based on Convolution
Neural Network (CNN) technique.
4. 2018 Mantoro, T., Ayu, Multi face recognition. Multi-Faces Recognition Process
6. 5. 2018 Chang L , Yang J, Li S, Xu H,
Liu K & Huan, C.
Based on stacked conventional and
sparse representation.
Face Recognition Based
on Stacked Convolutional
Auto encoder and Sparse
Representation.
6. 2018 MING Ju-wang Intelligent image Face Feature Dynamic
Recognition Method
Based on Intelligent Image
7. 2015 Mohd Yusuf Firoz Siddiqui
and Sukesha
Face Recognition using Original and
Symmetrical Face Images.
Based on symmetrical and
mirror images by
reflecting the original face
using Principal
Component Analysis and
some fusion techniques.
8. 2015 Hyung-Il Kim, Seung Ho Lee,
and Yong Man R
Improved face recognition Face Image Assessment
Learned With Objective
and Relative Face Image
Qualities for Improved
Face Recognition
9. 2018 iyush Kakkar, Mr. Vibhor
Sharma
Using face detection recognition Criminal Identification
System Using Face
Detection and Recognition
7. METHODOLOGY
In this project, we can detect and recognize the faces of the criminals in a video stream obtained from a
camera in real-time. The system consists of three databases. First is the citizen database, which will
contain the images and unique-id of all the citizens living in that country. Second is local watch list
database, which will have the images(min 10) and details(Unique-id, Name, Gender, Religion, Crimes
done) etc. of each criminal who belongs to that country. Third is International watch list database, which
will have the images (min 10) and details(Unique-id, Name, Gender, Religion, Crimes done) etc. of the
criminals who are not the citizens of that country. All the images are first preprocessed. Then it goes
through feature extraction where Haar cascade is used. The video is captured from the surveillance
camera which are converted into frames. When a face is detected in a frame, it is preprocessed. Then it
goes through feature extraction where Haar cascade is used. The features of the processed real-time
image is compared with the features of processed images which are stored in the citizen database. If a
match is found, it is further compared with the features of images stored in a local watch list database to
identify if the person is criminal or not. If he is criminal a notification is sent to the police personnel with
all the details and the time for which he was under the surveillance of the camera. If he is not a citizen of
that country, it is then compared with the features of images stored in the international watch list
database. If a match is found, a notification is sent to the police personnel with all the details and the
time for which he was under the surveillance of the camera. If a match is not found in both the watch
lists, he is innocent.
10. PROPOSED METHOD
• In this project, we can detect and recognize the faces of the criminals in a video stream obtained from a
camera in real-time. The system consists of three databases
• First is the citizen database, which will contain the images and unique-id of all the citizens living in that
country.
• Second is local watch list database, which will have the images (min 10) and of each criminal who
belongs to that country.
• Third is International watch list database, which will have the images (min 10) and details(Unique-id,
Name, Gender, Religion, Crimes done, etc) of the criminals who are not the citizens of that country.
• Open CV module and contains various functions for face detection and recognition.
• Then it goes through feature extraction where Haar cascade is used. The features of the processed real-
time image is compared with the features of processed images which are stored in the Google fire base.
• If a match is found, it is further compared with the features of images stored in a local watch list of
google fire base to identify if the person is criminal or not.
• Andriod app is used to get notification and details.
• If he is criminal a notification is sent to the mobile app with all the details and the time for which he
was under the surveillance of the camera.
• If a match is not found in both the watch lists, he is innocent.
11. REFERENCES
• 1] Nurul Azma Abdullah, Md. Jamri Saidi and Nurul Hidayah Ab Rahman “Face recognition for criminal
identification: An implementation of principal component analysis for face recognition”The 2nd
International Conference on Applied Science and Technology 2017 (ICAST’17)
• [2] Apoorva.P, Ramesh.B and Varshitha.M.R “Automated criminal identification by face recognition using
open computer vision classifiers” Third International Conference on Computing Methodologies and
Communication (ICCMC 2019).
• [3] Rasanayagam, K.Kumarasiri, S.D.D, Tharuka, W. A. D. Samaranayake, N. Samarasinghe and P.
Siriwardana “CIS: An Automated Criminal Identification System”. 2018 IEEE International Conference on
Information and Automation for Sustainability (ICIAfS)R. Nicole, “Title of paper with only first word
capitalized,” J. Name Stand. Abbrev., in press.
• [4] Mantoro, T., Ayu, M. A., & Suhendi. (2018).” Multi-Faces Recognition Process Using Haar Cascades and
Eigenface Methods” 2018 6th International Conference on Multimedia Computing and Systems (ICMCS).
• [5] Chang L , Yang J, Li S, Xu H, Liu K & Huan, C. (2018). ”Face Recognition Based on Stacked Convolutional
Autoencoder and Sparse Representation”. 2018 IEEE 23rd International Conference on Digital Signal
Processing (DSP).
• [6] MING Ju-wang (2018), “Face Feature Dynamic Recognition Method Based on Intelligent Image”.
International Conference on Virtual Reality and Intelligent Systems
12. [7] Mohd Yusuf Firoz Siddiqui and Sukesha (2015), “Face Recognition using Original and Symmetrical Face
Images”. 1st International Conference on Next Generation Computing Technologies (NGCT-2015)
[8] Hyung-Il Kim, Seung Ho Lee, and Yong Man R (2015), “Face Image Assessment Learned With Objective
and Relative Face Image Qualities for Improved Face Recognition.
[9] Piyush Kakkar, Mr. Vibhor Sharma (2018) “Criminal Identification System Using Face Detection and
Recognition”. International Journal of Advanced Research in Computer and Communication Engineering
[10] Lamiaa A. Elrefaei, Alaa Alharthi, Huda Alamoudi, Shatha Almutairi (2017) “Real-time Face Detection
and Tracking on Mobile Phones for Criminal Detection”.