![International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD47656 | Volume – 5 | Issue – 6 | Sep-Oct 2021 Page 1607
2. TECHNOLOGY STACK
OpenCV
It is a library of python bindings which is used to
solve computer vision problems. The cv2 module of
OpenCV is used for reading and writing images from
the video stream.
K Nearest Neighbors (KNN)
It is an algorithm used for classifying objects based
on the closest training examples in a feature space.
An object is classified by a majority vote of its
neighbors, with the object being assigned to the class
most common amongst its k nearest neighbors.
Haar Cascade Classifier
It is used for object detection; it detects the face in an
image based on various features. It has a cascade
function which is trained from a lot of positive and
negative images, based on the training objects can be
detected from other images.
3. CONCLUSION
The system is expected to be fully automated,
reliable, transparent and convenient. The whole
system can be practically implemented for luxury
trains and buses, airport bounded buses, metro
stations, airports especially at cities in India. This
helps to reduce chaos and ensures a smoother travel
for passengers onboard. For commuters, traveling is
unified, simplified, cost-effective and comfortable.
Railway authorities only have to authorize
commuters’ travel, no wastage of paper, security of
commuters is ensured, no cash money transaction
needs to be done and less staff and resources are
required. It is a practical and necessary upgradation to
the Railways which solves a number of problems in
the current system. Such advancements are necessary
considering the ever-increasing number of passengers
in order to make travelling a more enjoyable
experience.
REFERENCES
[1] Sharmila, Raman Sharma, Dhanajay Kumar,
Kritika Gautham, “Performance Analysis Of
Human Face Recognition Techniques” 2019
4th Interational Conference on Internet Of
Things: Smart Innovation and Usages (IoT-
SIU)
[2] Jannat Binta Alam, Md. Momarul Islam,
Taskeed Jabid, Sabbir Ahmed, “System
Development using Face Recognition” 2019
International Conference on Automation,
Computational and Technology Management
(ICACTM).
[3] Vishakha Mehta, Sarika Khandelwal, Ashish
Kumar Kumawat, “A Survey on Face
Recognition” 2018 2nd
International Conference
on Trends in Electronics and Informatics
(ICOEI).
[4] Lingxiao Wang, Yali Li, Shengjin Wang,
“Feature Learning for One-Shot Recognition”
2018 25th IEEE International Conference on
Image Processing (ICIP)
[5] Prasun Chowdhury, Poulami Bala, Diptadeep
Addy, Sumit Giri, Aritra Ray Chaudhuri,
“RFID and Android based smart ticketing and
destination announcement system” 2016
International Conference on Advances in
Computing, Communications and Informatics
(ICACCI).
[6] Rui Couto, João Leal, Pedro Mauricio Costa,
Teresa Galvão, “Exploring Ticketing
Approaches Using Mobile Technologies: QR
Codes, NFC and BLE” 2015 IEEE 18th
International Conference on Intelligent
Transportation Systems.
[7] Florian Schroff, Dmitry Kalenichenko, James
Philbin, “FaceNet: A unified embedding for
face recognition and clustering’ 2015 IEEE
Confernece on Computer Vision and Pattern
Recognition (CVPR).
[8] Rakesh Saini, Abhishek Saini, Deepak
Agarwal, “Analysis of Different Face
Recognition Algorithms” 2014 International
Journal of Engineering Research & Technology
(IJERT).
[9] Neeti Jain, Nagesh Sharma, Nishant Kamboj,
Ankur Kakani, “Analysis of Different Methods
for Face Recognition” 2014 Journal of
Innovative Computer Science & Engineering
Volume 1 Issue 2.](https://image.slidesharecdn.com/239facerecognitionusingmlinpublictransports-220307051411/85/Face-Recognition-Using-ML-in-Public-Transports-3-320.jpg)
Face Recognition Using ML in Public Transports
- 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 5 Issue 6, September-October 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD47656 | Volume – 5 | Issue – 6 | Sep-Oct 2021 Page 1605 Face Recognition Using ML in Public Transports Mangal Kotkar, Shubham Gawade, Shubham Mandavkar, Prathamesh Gujar IT Engineering, Dhole Patil College of Engineering, Pune, Maharashtra, India ABSTRACT Modernization of railways has forever been an issue focused on the development of the fundamental infrastructure of a nation. Since the railways represent one of the most effective modes of transport offered to the people, It is important to keep a check on the security issues that are arising in todays world. According to the need there must be an upgradation in systems we use. One such upgradation is that the role of Artificial Intelligence and e-ticketing that is achieved with the assistance of face recognition technology. This technology has been extensively employed as a biometric method and hence can be used for passenger verification. KEYWORDS: face recognition, ticket verification, k-nearest neighbours, OpenCV, haar cascade classifier How to cite this paper: Mangal Kotkar | Shubham Gawade | Shubham Mandavkar | Prathamesh Gujar "Face Recognition Using ML in Public Transports" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456- 6470, Volume-5 | Issue-6, October 2021, pp.1605- 1607, URL: www.ijtsrd.com/papers/ijtsrd47656.pdf Copyright © 2021 by author (s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0) 1. INTRODUCTION The main objective of the project is to design a system for contactless verification of passenger and avoid black marketing in the ticket booking system so that the passengers can enjoy a Hassle-free and delightful journey. Ticketing system for railways was introduced to manifest and authorize valid commuters to suit one’s comfort, purpose or wants while travelling. In todays scenario where the world is facing the Corona virus pandemic customers wish to have a “frictionless travel”, and a “frictionless experience”, whereas the current ticketing systems can discourage people from utilizing public transport as there is no such facility available. To overcome the this issues, we propose a ticket verification system using facial recognition that completely eliminates the idea of using paper tickets and will also be use full for stoping illegal business in tickets booking. 1.1. Literature Survey Various technologies have already been introduced in the past, some of them have been described in. According to some of the feasible technologies which can be used for railway ticketing are QR codes, NFC and BLE, each of them having their own pros and cons. Quick Response (QR) Codes allow the storage of information in a 2D barcode format, storing information both horizontally and vertically, thus carrying several hundred times more information than regular barcodes. These codes can be read by dedicated readers, or using smart phones as long as they have a camera and autofocus feature. Also, QR Codes have the advantage of being easily created and can be printed using a regular printer, thus making the process of physical distribution not expensive. Near Field Communication (NFC) is a short distance wireless technology, which comes embedded in some smart phones, that allows users to exchange information with a smart card or other NFC devices. A smart card consists of a passive NFC chip which can be read by a device called a reader which is an active NFC device. An NFC tag is, for instance, a small sticker containing an NFC chip, having stored data in it, that can be read or written by an active device. Bluetooth is a wireless technology, that exchanges data over short distances using radio transmissions. The most recent Bluetooth standard is IJTSRD47656
- 2. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD47656 | Volume – 5 | Issue – 6 | Sep-Oct 2021 Page 1606 called Bluetooth Smart, or Bluetooth Low Energy (BLE); it has several advantages over the traditional standard, such as lower power consumption and enhanced range, and it is being adopted by the recently released smart phones. These technologies have their own flaws, some of them being usage of paper, high cost, high user interaction, less secure etc. Another technology which was proposed in was the usage of RFID, this technology uses radio signals to exchange data between a mobile device equipped with memory and a host computer. RFID system consists of two essential components: a tag and a reader. The tag is analogous to the barcode label and it comes in different shapes and sizes. It contains an antenna connected to a small microchip. The reader functions similar to a barcode scanner. However, barcode scanner uses laser beams whereas the RFID reader uses electromagnetic waves. The drawback of this technology is that it is not cost effective as a huge initial investment is required to install RFID readers in the Railway Stations. Therefore, in this paper facial recognition technology is introduced to eliminate all the issues faced due to the previously implemented technologies. MOTIVATION In today’s world where everyone is facing Covid-19 pandemic people want a frictionless service to avoid getting affected from the virus. The proposed system helps us in providing the passenger a contact free service. Black marketing and using false identity document to book tickets is increasing day-by-day. If there is any illegal activity done by the passenger we can track him easily using the facial image. Also people travelling without ticket will not be allowed to board and hence it will reduce the corruption and provide good travel experience to the passengers. 1.2. The Proposed System The proposed method is to use facial recognition. Facial recognition system is a type of biometric identification which is used to identify a person from the digital image of his/her face. A complete process is divided into three steps where the first step is to find or create a database of faces with multiple images for each individual. The next step is to detect the faces in the database and utilize them to train the face recognizer and the final step is to test the face recognizer which was trained to recognize the images. In the proposed system the new users have to register themselves to the site. While doing the registration images of the person will be captured and trained. Once the registration is complete, the user can login anytime and book a ticket. While booking a ticket the user has to fill the basic details such as source, destination, and date and so on. When the user goes to the railway station and walks through the gateway, the camera will detect the face of the user and check if the user has booked a ticket. If the user has booked a ticket, then the gateway opens and the user can pass through it and if the user hasn’t bought a ticket then the gateway will remain closed. In this way this system prevents people without a ticket from travelling and it also eliminates the usage of a physical ticket. Step 1: Registration A new user has to register by entering the required details and the camera captures the image of the user which will be used for the verification. Step 2: Login The registered users can login to the portal to book the tickets by entering the username and password, if either of the fields are incorrect then an error is thrown. Step 3: Database Generation A database is created and all the details of the users including their images are stored in it. The images of the users will be trained using a specific algorithm. Step 4: Ticket Verification This phase takes place when the user walks into the gateway in the railway station. The camera installed above the gateway detects the user’s face and it checks with the database to verify if the user has booked a ticket. If the verification is true, then the user is permitted to enter the gateway otherwise the gateway will remain closed thus preventing the users without a ticket from proceeding with their journey. Advantages 1. Less Corruption 2. Time Saving 3. Less Resources Consumption 4. Faster and better process 5. Secure Application of the Proposed System 1. Ticket Automation 2. Attendance System System Architecture
- 3. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD47656 | Volume – 5 | Issue – 6 | Sep-Oct 2021 Page 1607 2. TECHNOLOGY STACK OpenCV It is a library of python bindings which is used to solve computer vision problems. The cv2 module of OpenCV is used for reading and writing images from the video stream. K Nearest Neighbors (KNN) It is an algorithm used for classifying objects based on the closest training examples in a feature space. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors. Haar Cascade Classifier It is used for object detection; it detects the face in an image based on various features. It has a cascade function which is trained from a lot of positive and negative images, based on the training objects can be detected from other images. 3. CONCLUSION The system is expected to be fully automated, reliable, transparent and convenient. The whole system can be practically implemented for luxury trains and buses, airport bounded buses, metro stations, airports especially at cities in India. This helps to reduce chaos and ensures a smoother travel for passengers onboard. For commuters, traveling is unified, simplified, cost-effective and comfortable. Railway authorities only have to authorize commuters’ travel, no wastage of paper, security of commuters is ensured, no cash money transaction needs to be done and less staff and resources are required. It is a practical and necessary upgradation to the Railways which solves a number of problems in the current system. Such advancements are necessary considering the ever-increasing number of passengers in order to make travelling a more enjoyable experience. REFERENCES [1] Sharmila, Raman Sharma, Dhanajay Kumar, Kritika Gautham, “Performance Analysis Of Human Face Recognition Techniques” 2019 4th Interational Conference on Internet Of Things: Smart Innovation and Usages (IoT- SIU) [2] Jannat Binta Alam, Md. Momarul Islam, Taskeed Jabid, Sabbir Ahmed, “System Development using Face Recognition” 2019 International Conference on Automation, Computational and Technology Management (ICACTM). [3] Vishakha Mehta, Sarika Khandelwal, Ashish Kumar Kumawat, “A Survey on Face Recognition” 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). [4] Lingxiao Wang, Yali Li, Shengjin Wang, “Feature Learning for One-Shot Recognition” 2018 25th IEEE International Conference on Image Processing (ICIP) [5] Prasun Chowdhury, Poulami Bala, Diptadeep Addy, Sumit Giri, Aritra Ray Chaudhuri, “RFID and Android based smart ticketing and destination announcement system” 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). [6] Rui Couto, João Leal, Pedro Mauricio Costa, Teresa Galvão, “Exploring Ticketing Approaches Using Mobile Technologies: QR Codes, NFC and BLE” 2015 IEEE 18th International Conference on Intelligent Transportation Systems. [7] Florian Schroff, Dmitry Kalenichenko, James Philbin, “FaceNet: A unified embedding for face recognition and clustering’ 2015 IEEE Confernece on Computer Vision and Pattern Recognition (CVPR). [8] Rakesh Saini, Abhishek Saini, Deepak Agarwal, “Analysis of Different Face Recognition Algorithms” 2014 International Journal of Engineering Research & Technology (IJERT). [9] Neeti Jain, Nagesh Sharma, Nishant Kamboj, Ankur Kakani, “Analysis of Different Methods for Face Recognition” 2014 Journal of Innovative Computer Science & Engineering Volume 1 Issue 2.