Security system based on real time face recognition
Ipinnimo Oluwaseyi Ayodeji; Supervisor: Ziya Dereboylu
- Nicosia Cyprus International University 2014
- X, 89 p. table 30.5 cm CD
Includes CD
Includes references (76-82 p.)
CHAPTER 1 1 INTRODUCTION 1 BIOMETRIC SYSTEM 2 Applications 3 Biometric error 3 INTRODUCTION TO FACE RECOGNITION 4 Problem that may occur during face recognition 5 Scale invariance 5 Shift invariance 5 Illumınatıon invariance 6 Emotional expression and detail invariance 6 Noise invariance 6 GOAL 7 OVERVIEW 7 CHAPTER 2 8 LITERATURE SURVEY 8 INTRODUCTION 8 FACE DETECTION 8 Skin tone face detection 9 Viola-jones face detection algorithm 11 Local SMQT based face detection 11 FACE RCOGNITION 14 Background and related work 14 Outline of a typical face recognition system 16 The Image acquisition module 16 The pre-processing module 17 The feature extraction module 19 The classification module 19 Tranning set 19 Face Library or Face database 20 Coventional techniques in face recognition 20 Correlation 20 Linear Discriminant Analysis 21 Independent Component Analysis 23 Other conventional methods for face recognition 24 Local features analysis 25 Nonnegative matirx factorization 26 Increments nonnegative matrix factorization 27 Histogram based method for face recognition 29 Local binary pattern for face recognition 29 PDF based face recognition system 30 CHAPTER 3 32 FACE RECOGNITION USING PCA 32 INTRODUCTION 32 EIGENFACES CALCULATION 37 USING EIGENFACES TO CALSSIFY A FACE IMAGE 42 REBUILDING A FACE IMAGE WITH EIGENFACES 43 CHAPTER 4 46 EMBEDDED SYSTEM 46 INTRODUCTION 46 EMBEDDED SYSTEM 46 EMBEDDED SYSTEM USED IN THIS RESEARCH 48 De-bounced switch 48 The switching circuit (LCD) 49 Electric Lock 50 Liquid Crystal Display (LCD) 50 Microcontroller (UC) PIC18F4550 52 Overview of the USB peripheral 53 USB Status and Control 54 IMPLEMENTATION OF THE ES 55 Communication with the ES 56 CHAPTER 5 57 TESTING AND RESULT 57 DATA SETS 57 AT & T database of faces(formerly known as ORL database of faces ) 57 Face Recognition data, university of ESSEX, UK 58 Personal data set 58 SYSTEM HARDWARE FOR TESTING 58 SYSTEM SOFTWARE FOR TESTING 59 TEST RESULT FOR AT&T DATABASE OF FACES 60 TEST RESULT FOR ESSEX DATA SET 62 TEST WITH PERSONAL DATA SET WITH NO FACE DETECTION 64 TEST WITH PERSONAL DATA SET WITH FACE DETECTION 69 OVERALL TEST OF THE SYSTEM 71 CHAPTER 6 73 CONCLUSION AND FURTHER RESEARCH 73 CONCLUSION 73 FURTHER RESEARCH 74 REFERENCES 76
'ABSTRACT Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatically recognize people's face. Face recognition systems play a vital role in many applications including surveillance, biometrics and security as the rapid development of computer science and pattern recognition, visual technology is widely applied in industry and daily life. The goal of this research is to implement face recognition on a real life platform for security system. The software should be able to take an image of a person through the camera connected to the USB of a computer, recognize the individual based on previously stored samples and grant access or deny access through a device connected to another port of the same computer. The method that will be considered is principal component analysis (PCA) method for face recognition. A database of trained images is stored on the computer for recognizing a test image. The image from the camera is resized and cropped with MATLAB. This image is further processed by comparing it with images in the database. If it matches any of the images in the database, a signal is sent to a device connected to another USB port (Embedded system) to grant access for the person that the image is taken from. If the image does not match any of the images in the database then, the accessis denied for the person. Key words: Face recognition, PCA, Embedded system'
Yerleşik sistem Embedded system Yüz tanıma Face recognition