Sabiqul Islam, Remon Ahmed and Sadia Afrin Tisha at the Department of Computer Science and Engineering, Daffodil International University, have been accepted as satisfactory in partial fulfillment of the requirements for the degree of B.Sc. We hereby declare that this project was done by us under the supervision of Mohammad Jahangir Alam, Lecturer, Department of CSE Daffodil International University. We are very grateful and extend our sincere thanks to Mohammad Jahangir Alam, Lecturer, Department of CSE Daffodil International University, Dhaka.
Deep knowledge and great interest of our supervisor in the field of "Machine Learning" to realize this project. Syed Akhter Hossain, Professor and Head of CSE Department, for his kind help to complete our project and also to other faculty members and staff of CSE department of Daffodil International University. We would like to thank all our course fellow at Daffodil International University who participated in this discussion during the completion of the course work.
In this system, all student data by time and by class will be stored in the database. Taking attendance and keeping record is the most complicated thing in manual attendance system. In recent years, some automated systems have developed significantly such as fingerprints, QR code technology.
With this system, no one can act as an intermediary for another who is not present in the class.
Motivation 1
Manual attendance, such as entering name and identification on paper, is a daunting system to ever take attendance. Taking the participation one by one takes a huge amount of time and has a lot of potential to become a broker. Tracking systems based on facial recognition take less time and less chance to be an intermediary to overcome this problem.
In our proposed method we have used a machine learning based Haar cascade technique for face detection and recognition for its robustness in image detection with Haar Cascade we use LBPH algorithm for face recognition. By using this technology together with facial recognition, the proxy will be significantly reduced and a huge amount of time can be saved.
Objectives 2
The main approach of paper [1] which needs straight images of a student and stored in the database is further used for recognition by matching the input images in real time. After the photo was captured in camera, it was then exposed to 3D modeling and canonical techniques were applied to the photo for comparison. In these images are converted to Eigenfaces and then recognized by comparing the Eigenfaces form the input image with the database.
In the paper [11], they proposed a face detection and recognition based student attendance system using a convolutional neural network. Although it is a facial recognition based attendance system, the students have to keep the same faces stored in the database. When someone keeps his beard long, it is difficult to recognize these faces, so this system needs to update the database very often.
This system consists of two parts: the first part HaarCascade algorithm which is used for face detection and converting the RGB image to grayscale and then the LBPH algorithm which is used to recognize and attend to the face. Everyone has to go in front of the camera, more than 90 photos are needed for a single student and this data has to be stored with the person's ID and name. When a student needs to register in this system, he/she stands in front of the camera and the camera starts capturing the image of the individual. After capturing more than 90 images, the camera will automatically turn off and the registrar will set the ID and name and save it to the profile.
In this system, the LBPH algorithm with the Haar cascade method performs tremendously better than other classification methods. This system can classify accurately even in low light and when the head is tilted. But if we want the maximum recognition rate, then we have to take it during the daytime.
During the day, this system provides 93% accuracy for recognizing images that are already stored in the dataset. In this photo, we can see that the system can capture several faces at the same time, and the system also recognizes a face that is not at a 90-degree angle to the camera. Press the Q button to complete the process and the ID name with date will be displayed on the GUI in the attendance section.
This saved name and ID will be saved in the Excel sheet located in the project's presence folder.
Expected Output 2
Report Layout 2
Background 3-5
Related Works 3
If neighboring pixel values are lower than the central pixel, it will be written 0 and those are greater than or equal to the central pixel value, then it will be written 1.
Scope of Problem 5
Challenges 5
Research Methodology 6-15
- Proposed methodology 6
- Research Subject and Instrumentation 7
- Data collection procedure 10
- System model 12
- Face detection 14
- Face recognition 15
Python's simple syntax and many libraries make it superior to other object-oriented programming languages. It includes linear and non-linear image filtering, histograms, color space conversion and many things we need for image processing. Then the Haar classifier extracts the features from the RGB images, sets the ROI and converts it to grayscale and stores it in the database.
LBPH is a very popular and effective face recognition model and is a significant improvement over Eigenfaces and Fisherfaces. Once we have identified a face and know what a face looks like, we are now trying to determine who this face belongs to. We can see from the picture several pixels that are generated from each local point and there is a central pixel that compares this to its neighboring pixels.
Each single image generates many decimal numbers and these numbers will be plotted in a histogram.
Experimental Results and Discussion 17-20
Testing and Results 17
We can see that the system can easily recognize the existing face accurately even at night in a low light condition.
Conclusion and Future Scope 21-21
Scope for future development 21
