REAL TIME FACE ATTENDANCE SYSTEM USING DEEP LEARNING

ABSTRACT: Face is the representation of one’s identity. Hence, we have proposed an automated student attendance system based on face recognition. Face recognition system is very useful in life applications especially in security control systems. The airport protection system uses face recognition to identify suspects and FBI (Federal Bureau of Investigation) uses face recognition for criminal investigations. In our proposed approach, firstly, video framing is performed by activating the camera through a user- friendly interface. The face ROI is detected and segmented from the video frame by using Viola-Jones algorithm. In the pre-processing stage, scaling of the size of images is performed if necessary in order to prevent loss of information. The median filtering is applied to remove noise followed by conversion of colour images to grayscale images. After that, contrast-limited adaptive histogram equalization (CLAHE) is implemented on images to enhance the contrast of images. In face recognition stage, enhanced local binary pattern (LBP) and principal component analysis (PCA) is applied correspondingly in order to extract the features from facial images. In our proposed approach, the enhanced local binary pattern outperform the original LBP by reducing the illumination effect and increasing the recognition rate. Next, the features extracted from the test images are compared with the features extracted from the training images. The facial images are then classified and recognized based on the best result obtained from the combination of algorithm, enhanced LBP and PCA. Finally, the attendance of the recognized student will be marked and saved in the excel file. The student who is not registered will also be able to register on the spot and notification will be given if students sign in more than once. The average accuracy of recognition is 100 % for good quality images, 94.12 % of low-quality images and 95.76 % for Yale face database when two images per person are trained.

 

TABLE OF CONTENTS

DECLARATION ii

APPROVAL FOR SUBMISSION

ACKNOWLEDGEMENTS

ABSTRACT

TABLE OF CONTENTS

LIST OF TABLES 

LIST OF FIGURES

LIST OF SYMBOLS / ABBREVIATIONS

LIST OF APPENDICES

CHAPTER

1. INTRODUCTION 1

   1. Background 1

   2. Problem Statement 3

   3. Aims and Objectives 4

   4. Thesis Organization 4

2. LITERATURE REVIEW 5

   1. Student Attendance System 5

   2. Face Detection 6

      1. Viola-Jones Algorithm 10

   3. Pre-Processing 12

   4. Feature Extraction 16

      1. Types of Feature Extraction 20

   5. Feature Classification And Face Recognition 21

   6. Evaluation 22

3. METHODOLOGY 24

   1. Methodology Flow 24

   2. Input Images 27

      1. Limitations of the Images 28

   3. Face Detection 29

      1. Pre-Processing 29

         1. Scaling of Image 29

         2. Median Filtering 30

         3. Conversion to Grayscale Image 31

         4. Contrast Limited Adaptive Histogram Equalization 32

   4. Feature Extraction 32

      1. Working Principle of Original LBP 33

      2. Working Principle of Proposed LBP 34

      3. Working Principle of PCA 37

      4. Feature Classification 40

      5. Subjective Selection Algorithm and Face Recognition 40

4. RESULT AND DISCUSSION 42

   1. Result 42

   2. Discussion 46

   3. Comparison of LBP and PCA 50

   4. Comparison with Previous Researches 51

   5. Comparison with Luxand Face Recognition Application 54

   6. Weakness of the Algorithm 55

   7. Problems Faced and Solutions Taken 57

5. CONCLUSION AND RECOMMENDATION 58

   1. Conclusion 58

   2. Recommendation 59

REFERENCES 60