Docsity
Docsity
Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity

Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan

Guidelines and tips
Guidelines and tips
Sell on Docsity
Sell on Docsity
Docsity AI
Docsity AI
Log inSign up

Prepare for your exams

Study with the several resources on Docsity

Find documents

Prepare for your exams with the study notes shared by other students like you on Docsity

Search for your university

Find the specific documents for your university's exams

Docsity AINEW

Summarize your documents, ask them questions, convert them into quizzes and concept maps

Explore questions

Clear up your doubts by reading the answers to questions asked by your fellow students

Earn points to download

Earn points by helping other students or get them with a premium plan

Share documents
download point icon20 Points

For each uploaded document

Answer questions
download point icon5 Points

For each given answer (max 1 per day)

All the ways to get free points
Get points immediately

Choose a premium plan with all the points you need

Study Opportunities

Choose your next study program

Get in touch with the best universities in the world. Search through thousands of universities and official partners

Community

Ask the community

Ask the community for help and clear up your study doubts

Free resources

Our save-the-student-ebooks!

Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors

Face Recognition-based Attendance Management System, Slides of Artificial Intelligence

Savitribai Phule Pune UniversityArtificial Intelligence

This research paper explores the implementation of a face recognition system for managing attendance in educational settings. The document delves into the methodology, benefits, and technical aspects of using facial recognition technology for this purpose. The paper also discusses the use of opencv and insight face, two open-source libraries, in the development of the system.

Typology: Slides

2023/2024

Uploaded on 04/10/2024

kouls-official
kouls-official 🇮🇳

1 document

1 / 8

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
Face Recognition-based Attendan ce Management System
Section A-Research paper
720
Eur. Chem. Bull. 2023,12(Special Issue 9), 720-727
ISSN 2063-5346
FACE RECOGNITION-BASED
ATTENDANCE MANAGEMENT SYSTEM
Ms. Vinaya Kulkarni1
Article History: Received: 10.05.2023 Revised: 29.05.2023 Accepted: 09.06.2023
Abstract
The processes now in place to record and report attendance are laborious and time consuming.
When attendance is recorded manually, mistakes are more likely to occur. One of the most
popular biometric methods now in use is facial recognition. It has a wide variety of potential
uses, including but not limited to those of identification, authentication, and security. The goal
of this system is to replace the current manual attendance system with a fully automated one.
The first half of this system is already live and contains features like Redis database integration,
facial recognition, registration forms, and database access. During the second stage, we use
tools like Streamlit and Dashboard.
Keywords : OpenCV, insightface, Redis Database , Streamlit.
1Assistant Professor, Changu Kana Thakur Arts, Commerce and Science College,
New Panvel (Autonomous)
[email protected]
DOI:10.48047/ecb/2023.12.9.71
pf3
pf4
pf5
pf8

Partial preview of the text

Download Face Recognition-based Attendance Management System and more Slides Artificial Intelligence in PDF only on Docsity!

Eur. Chem. Bull. 2023 ,12(Special Issue 9), 720 - 727

ISSN 2063- 5346 FACE RECOGNITION-BASED

ATTENDANCE MANAGEMENT SYSTEM

Ms. Vinaya Kulkarni^1 Article History: Received: 10.05.2023 Revised: 29.05.2023 Accepted: 09.06. Abstract The processes now in place to record and report attendance are laborious and time consuming. When attendance is recorded manually, mistakes are more likely to occur. One of the most popular biometric methods now in use is facial recognition. It has a wide variety of potential uses, including but not limited to those of identification, authentication, and security. The goal of this system is to replace the current manual attendance system with a fully automated one. The first half of this system is already live and contains features like Redis database integration, facial recognition, registration forms, and database access. During the second stage, we use tools like Streamlit and Dashboard. Keywords : OpenCV, insightface, Redis Database , Streamlit. (^1) Assistant Professor, Changu Kana Thakur Arts, Commerce and Science College, New Panvel (Autonomous) [email protected] DOI:10.48047/ecb/2023.12.9. 71

Eur. Chem. Bull. 2023 ,12(Special Issue 9), 720 - 727 Introduction Everything in the modern day has gone contactless, from business administration to classroom instruction to monetary transactions. Students' arrival and leave from the classroom can now be recorded reliably and conveniently through the use of contactless attendance systems. Successful preventative measures against the COVID- 19 epidemic include going touchless. Taking attendance the old fashioned way is a time-consuming and laborious process in classroom settings. Probabilities of a proxy showing up are not zero. As a result, a variety of alternative methods, including RFID, iris recognition, fingerprint recognition, and so on, have been implemented by numerous educational institutions as a means of keeping track of students' attendance.[4] In recent years, face recognition has emerged as a highly effective method for analyzing and interpreting images, making it one of the most widely studied issues in computer vision. Some of the most common applications involve discovering missing persons, investigating retail crimes, identifying security threats, locating social media profiles, monitoring classroom attendance, and identifying drivers. Incorporating face recognition into everyday life has the potential to make it a safer, smarter, and more useful place. Methods typically used in facial recognition include: Face Detection: Face detector algorithms locate faces, draw bounding boxes around faces, and keep the coordinates of bounding boxes. [ 5 ]

  1. Face Alignments: Normalize the faces to be consistent with the training database.[ 5 ]
  2. Feature Extraction: Extract features of faces that will be used for training and recognition tasks.[ 5 ]
  3. Face Recognition: Matching the face against one or more known faces in a prepared database.[ 5 ] Traditional face recognition technology has some drawbacks. For each person to train a model, massive sample sizes of photos are needed (200 to at least 100 photos per person). Unable to add additional person details to a trained model. Retraining the model with all the data is necessary. As the number of people classified increases, the model's accuracy decreases. Prolonged processing time. Fast Face Recognition Model has benefits such as Fewer samples being needed, For a new person no model retraining is necessary, It is accurate enough and It has quick processing. Face recognition, machine learning, artificial intelligence, and other fields use OpenCV, a Python open-source package, for computer vision. The term "OpenCV" refers to the Open-Source Computer Vision Library, which is frequently used for image identification or recognition. In 1999, Intel gave it its official debut. Early versions were written in C/C++, although Python is also frequently used for computer vision today. Insight Face is an integrated Python library for 2D&3D face analysis. Insight Face efficiently implements a rich variety of state-of-the-art algorithms for face recognition, face detection, and face alignment, which optimized for both training and deployment. Research institute and industrial organization can get benefits from Insight Face library.[ 6 ] Literature Review Authors in [ 4 ] proposed a model of a face recognition-based attendance system. The four stages of this system are database building, face detection, face recognition, and attendance updating. Images of the students in class are used to develop databases. The Haar-Cascade classifier and the Local Binary Pattern Histogram technique are used, respectively, for face detection and recognition. Faces are picked up and recognized in the classroom's live- streamed footage. Attendance will be

Eur. Chem. Bull. 2023 ,12(Special Issue 9), 720 - 727 interpretation of a variety of widely used distance measures. The shortest distance between two vectors is represented by the Euclidean Distance. It is the sum of squares of differences between corresponding elements, or its square root. The L2-norm of a difference between vectors and vector spaces is equivalent to the Euclidean distance metric. It is determined mathematically using Pythagoras' Theorem. The sum of the squares along each perpendicular ordinate represents the overall distance between two objects. Euclidean Distance may be expressed as: The Manhattan Distance is the total of all the absolute disparities between two points in all the dimensions. Manhattan Distance may be expressed as: The cosine similarity index calculates how similar two vectors in an inner product space are to one another. It establishes whether two vectors roughly point in the same direction by calculating the cosine angle between them. The cosine similarity may be expressed as: An open-source Python toolkit called Streamlit makes it simple to develop and distribute stunning, personalized web apps for data science and machine learning. Using Streamlit App, an attendance app was developed. Results and Discussion The project's basic operating premise is that video data is transformed into images in order to be detected and recognised. Additionally, attendance includes the student's recognisable photograph. Figure 1: Home Page Figure 2 : Dataset

Eur. Chem. Bull. 2023 ,12(Special Issue 9), 720 - 727 Figure 3: Registration Figure 4 : Registered Data Figure 5: Real Time Prediction Figure 6 : Log Report Conclusion A face-recognition automatic attendance system automatically determines the name of the person. The suggested application can identify registered teachers and students. For individuals who don't register, this system identifies the unregistered. Each day, note when students enter and leave. Obtain the instructor's and the student’s daily attendance records. References

  1. Chaitra T.K, M.C. Chandrashekhar, Dr. M.Z. Kurian [August 2018] , “Attendance Management System Using Face Recognition”, JETIR , Volume 5, Issue 8 , ISSN- 2349 - 5162
  2. Principal, S. H. M., Mishra, A., Sharma, J. K., Aarif, M., & Arwab,

M. SMART AND INNOVATIVE

IDEAS TO PROMOTE TOURISM

FOR GLOBAL TRADE AND

ECONOMIC GROWTH.

  1. Ebrahimi, M., Attarilar, S., Gode, C., Kandavalli, S. R., Shamsborhan, M., & Wang, Q. (2023). Conceptual Analysis on Severe Plastic Deformation Processes of Shape Memory Alloys: Mechanical Properties and Microstructure Characterization. Metals , 13 (3),
  2. J. K. S. Al-Safi, A. Bansal, M. Aarif, M. S. Z. Almahairah, G. Manoharan and F. J. Alotoum, "Assessment Based On IoT For Efficient Information Surveillance Regarding Harmful Strikes Upon Financial Collection," 2023 International Conference on Computer Communication and Informatics

Eur. Chem. Bull. 2023 ,12(Special Issue 9), 720 - 727 Management Difficulties in the Internet of Things," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 322 - 326, doi: 10.1109/IC3I56241.2022. .

  1. Dhas, D. S. E. J., Raja, R., Jannet, S., Wins, K. L. D., Thomas, J. M., & Kandavalli, S. R. (2023). Effect of carbide ceramics and coke on the properties of dispersion strengthened aluminium‐silicon7‐ magnesium hybrid composites. Materialwissenschaft und Werkstofftechnik, 54(2), 147-157.
  2. Prabha, C., Arunkumar, S. P., Sharon, H., Vijay, R., Niyas, A. M., Stanley, P., & Ratna, K. S. (2020, March). Performance and combustion analysis of diesel engine fueled by blends of diesel+ pyrolytic oil from Polyalthia longifolia seeds. In AIP Conference Proceedings (Vol. 2225, No. 1, p. 030002). AIP Publishing LLC.
  3. Abd Algani, Y. M., Caro, O. J. M., Bravo, L. M. R., Kaur, C., Al Ansari, M. S., & Bala, B. K. (2023). Leaf disease identification and classification using optimized deep learning. Measurement: Sensors , 25 , 100643.
  4. Ratna, K. S., Daniel, C., Ram, A., Yadav, B. S. K., & Hemalatha, G. (2021). Analytical investigation of MR damper for vibration control: a review. Journal of Applied Engineering Sciences , 11 (1), 49-52.
  5. Abd Algani, Y. M., Ritonga, M., Kiran Bala, B., Al Ansari, M. S., Badr, M., & Taloba, A. I. (2022). Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm. Measurement: Sensors , 23 , 100406. https://doi.org/10.1016/j.measen.
    2. Mourad, H. M., Kaur, D., & Aarif, M. (2020). Challenges Faced by Big Data and Its Orientation in the Field of Business Marketing. International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) , 10 (3), 8091 - 8102.
    3. Ruban, S. R., Jayaseelan, P., Suresh, M., & RatnaKandavalli, S. (2020, December). Effect of textures on machining of carbon steel under dry cutting condition. In IOP Conference Series: Materials Science and Engineering (Vol. 993, No. 1, p. 012143). IOP Publishing.
    4. Naidu, K. B., Prasad, B. R., Hassen, S. M., Kaur, C., Al Ansari, M. S., Vinod, R., ... & Bala, B. K. (2022). Analysis of Hadoop log file in an environment for dynamic detection of threats using machine learning. Measurement: Sensors , 24 , 100545.
    5. Suman, P., Bannaravuri, P. K., Baburao, G., Kandavalli, S. R., Alam, S., ShanthiRaju, M., & Pulisheru, K. S. (2021). Integrity on properties of Cu-based composites with the addition of reinforcement: A review. Materials Today: Proceedings , 47 , 6609-6613.
    6. Kandavalli, S. R., Rao, G. B., Bannaravuri, P. K., Rajam, M. M. K., Kandavalli, S. R., & Ruban, S. R. (2021). Surface strengthening of aluminium alloys/composites by laser applications: A comprehensive review. Materials Today: Proceedings , 47 , 6919-6925.
    7. Sharma, Nisha, Anil Kumar Yadava, Mohd Aarif, Harishchander Anandaram, Ali Alalmai, and Chandradeep Singh. "Business Opportunities And Challenges For Women In The Travel And Tourism Industry During Pandemics Covid- 19." Journal of Positive School Psychology (2022): 897-903.

Eur. Chem. Bull. 2023 ,12(Special Issue 9), 720 - 727

  1. Raja, R., Jegathambal, P., Jannet, S., Thanckachan, T., Paul, C. G., Reji, S., & Ratna, K. S. (2020, November). Fabrication and study of Al6061-T6 reinforced with TiO nanoparticles by the process of friction stir processing. In AIP Conference Proceedings (Vol. 2270, No. 1, p. 030002). AIP Publishing LLC.
  2. Face Recognition Attendance System Using Python (analyticsvidhya.com)
  3. InsightFace: an open source 2D&3D deep face analysis library
  4. Kumar, B., & Kumar, P. (2022). Preparation of hybrid reinforced aluminium metal matrix composite by using ZrB2: A systematic review. Materials Today: Proceedings.
  5. Kandavalli, S. R., Khan, A. M., Iqbal, A., Jamil, M., Abbas, S., Laghari, R. A., & Cheok, Q. (2023). Application of sophisticated sensors to advance the monitoring of machining processes: analysis and holistic review. The International Journal of Advanced Manufacturing Technology , 1-26.
  6. Aarif, Mohd, and Ali Alalmai. "Importance of Effective Business Communication for promoting and developing Hospitality Industry in Saudi Arabia." A case study of Gizan (Jazan) (2019).
  7. Abd Algani, Y. M., Ritonga, M., Kiran Bala, B., Al Ansari, M. S., Badr, M., & Taloba, A. I. (2022). Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm. Measurement: Sensors , 23 , 100406. https://doi.org/10.1016/j.measen.
  9. Smitha, Pavithra S Hegde, Afshin [ May-2020],” Face Recognition based Attendance Management System”, IJERT, ISSN:2278-0181, Vol. 9 Issue 05
  11. Mourad, H. M., Kaur, D., & Aarif, M. (2020). Challenges Faced by Big Data and Its Orientation in the Field of Business Marketing. International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) , 10 (3), 8091 - 8102.
  12. Nandhini R, Duraimurugan N, S.P.Chokkalingam [ February 2019], “Face Recognition Based Attendance System”, IJEAT, ISSN: 2249 – 8 958, Volume- 8 , Issue-3S
  13. OpenCvDocumentation - https://opencv.org