臺灣博碩士論文加值系統

Nowadays, technology is growing rapidly followed by modernization. Face detection technology is one technology that has been developed and applied in various sectors such as biometrics recognition systems, retrieval systems, database indexing in digital video, security systems with restricted area access control, video conferencing, and human interaction systems. From various sectors that could be developed, emerging new ideas to apply digital image face detection results further, namely eye detection. Eye detection is a further development of face detection in which the image of a human face was detected to be processed by detecting the location of both eyes on the face. Nowadays, the eye detection system can be used as a means of developing more complex applications and can be applied directly in the aspect of technology that uses eye detection like, eye state detection system, drowsiness and fatigue detection system, safety driving support systems or driver assistance system.
In this research, a real-time eye state detection system using Haar Cascade Classifierand Circular Hough Transform (CHT) is presented. This system first detects the face and then the eyes using Haar Cascade Classifiers, which differentiate between open and closed eyes. CHT is used to detect the circular shape of the eye and also used to enhance the performance of Haar Cascade Classifier while detecting eyes. When the classifiers are not correctly classifying the eye,then the CHT will detect the circular shape in the detected eye region.
The accuracy of face detection is 97.60% and eye detection is 98.56% for our database which contains 2856 images for open eye and 2384 images for closed eye. This system works on several stages and is fully automatic. This eye state detection system was tested by several people, and the overall accuracy of the proposed system is 96.96%.

Abstract............................................i
Acknowledgement.....................................ii
List ofContent................................... ..iii
List of Figure......................................v
List of Table.......................................vii
List of Equation....................................viii
Chapter 1 Introduction..............................1
1.1 Motivation..................................1
1.2 Contribution................................2
1.3 Thesis Organization.........................2
Chapter 2 Related Work..............................3
2.1 Haar-like Features...........................3
2.2 Cascade Classifiers..........................4
2.3 Circular Hough Transform (CHT)...............6
Chapter 3 Proposed System...........................9
3.1 System Overview..............................9
3.2 Proposed Algorithm...........................11
3.3 Face and Eye Detection.......................12
3.3.1 Features...............................12
3.3.1.1 Integral Image....................13
3.3.2 Learning Classification Functions......14
3.3.3 Creating Haar Cascade Classifier.......15
3.4 Iris Detection...............................22
3.5 Eye State Detection..........................24
3.5.1 Eye State Analysis.....................24
3.5.2 Drowsiness Analysis....................25
Chapter 4 Experimental Result.......................26
4.1 Experiment...................................26
4.2 Results......................................28
4.3 Limitation and Discussion....................36
Chapter 5 Conclusion................................37
5.1 Conclusion...................................37
5.2 Future Work..................................38
References..........................................39

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