IMAGE PROCESSING AND FACE DETECTION FOR FACE RECOGNITION SYSTEM

<p align="justify">Face recognition is a personal identification system which uses a person's personal characteristic (in this case the person's face) to identify the person's identity. The system developed in this final project is a face recognition system which uses...

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Main Author: (NIM 13203127), ROBIN
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/11320
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:11320
spelling id-itb.:113202017-09-27T10:18:43ZIMAGE PROCESSING AND FACE DETECTION FOR FACE RECOGNITION SYSTEM (NIM 13203127), ROBIN Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/11320 <p align="justify">Face recognition is a personal identification system which uses a person's personal characteristic (in this case the person's face) to identify the person's identity. The system developed in this final project is a face recognition system which uses FLD (Fisher Linear Discriminant) for feature extraction. The system's designing process is divided into 2 phases: face detection and face recognition phases.<p align="justify"><p>The focus of this paper is on the face detection phase. The input image is a digital image which will be processed through several normalization processes. The normalization processes designed in this project include scale and lighting normalization. The normalization processes are needed to improve the success rate of the classifier. The classifier is used to discriminate between a face image and a non face image. In this final project, SVM (Support Vector Machine) is used as the classifer.<p align="justify"><p>By combining the FLD feature extraction method and SVM as classifier, the system would be able classify part of the input image which contain a face image from the whole input image. The output of the face detection system developed in this project is a face image that is already normalized. This normalized image will then processed by the face recognition system for recognition.<p align="justify"><p>Tests conducted in this final project resulted in 83% (83 correct images detection and recognition out of 100 test images) accuracy rate of the overall recognition system. This accuracy rate is high considering that the system developed in this project is able to identify individuals with different poses from the database. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description <p align="justify">Face recognition is a personal identification system which uses a person's personal characteristic (in this case the person's face) to identify the person's identity. The system developed in this final project is a face recognition system which uses FLD (Fisher Linear Discriminant) for feature extraction. The system's designing process is divided into 2 phases: face detection and face recognition phases.<p align="justify"><p>The focus of this paper is on the face detection phase. The input image is a digital image which will be processed through several normalization processes. The normalization processes designed in this project include scale and lighting normalization. The normalization processes are needed to improve the success rate of the classifier. The classifier is used to discriminate between a face image and a non face image. In this final project, SVM (Support Vector Machine) is used as the classifer.<p align="justify"><p>By combining the FLD feature extraction method and SVM as classifier, the system would be able classify part of the input image which contain a face image from the whole input image. The output of the face detection system developed in this project is a face image that is already normalized. This normalized image will then processed by the face recognition system for recognition.<p align="justify"><p>Tests conducted in this final project resulted in 83% (83 correct images detection and recognition out of 100 test images) accuracy rate of the overall recognition system. This accuracy rate is high considering that the system developed in this project is able to identify individuals with different poses from the database.
format Final Project
author (NIM 13203127), ROBIN
spellingShingle (NIM 13203127), ROBIN
IMAGE PROCESSING AND FACE DETECTION FOR FACE RECOGNITION SYSTEM
author_facet (NIM 13203127), ROBIN
author_sort (NIM 13203127), ROBIN
title IMAGE PROCESSING AND FACE DETECTION FOR FACE RECOGNITION SYSTEM
title_short IMAGE PROCESSING AND FACE DETECTION FOR FACE RECOGNITION SYSTEM
title_full IMAGE PROCESSING AND FACE DETECTION FOR FACE RECOGNITION SYSTEM
title_fullStr IMAGE PROCESSING AND FACE DETECTION FOR FACE RECOGNITION SYSTEM
title_full_unstemmed IMAGE PROCESSING AND FACE DETECTION FOR FACE RECOGNITION SYSTEM
title_sort image processing and face detection for face recognition system
url https://digilib.itb.ac.id/gdl/view/11320
_version_ 1822016560559030272