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”.
