Face recognition 1
Facial recognition software has been a hot topic for research due to its practicality in today’s society, be it in security applications such as identifying a suspect from an image source or video source, or in schools where face recognition technology can be used for attendance taking. It has be...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2016
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/67584 |
| _version_ | 1826112670772756480 |
|---|---|
| author | Chua, Glen Jun Xiong |
| author2 | Chua Chin Seng |
| author_facet | Chua Chin Seng Chua, Glen Jun Xiong |
| author_sort | Chua, Glen Jun Xiong |
| collection | NTU |
| description | Facial recognition software has been a hot topic for research due to its practicality in today’s society, be it in security applications such as identifying a suspect from an image source or video source, or in schools where face recognition technology can be used for attendance taking.
It has been observed that the accuracy and reliability of the face recognition system depends on many factors. Some of them include: the angle at which the face is facing the camera, the background noise accompanying the image source or video source, and lastly, the algorithm used for both face detection and recognition.
This paper aims to evaluate the effectiveness of face recognition systems using primarily the viola-jones object detection framework for face detection and Principal Component Analysis (PCA) for face recognition. This is done by evaluating a face sample, either from an image source or from a live video source against a reliable database of faces. Thus, the reliability of the face recognition system can then be measured. Last but not least, the technique of Principal Component Analysis is compared to other face recognition techniques, specifically the Fisher Linear Discriminating (FLD) approach and the Linear Discriminant Analysis. (LDA) |
| first_indexed | 2024-10-01T03:10:53Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/67584 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:10:53Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/675842023-07-07T17:02:52Z Face recognition 1 Chua, Glen Jun Xiong Chua Chin Seng School of Electrical and Electronic Engineering DRNTU::Engineering Facial recognition software has been a hot topic for research due to its practicality in today’s society, be it in security applications such as identifying a suspect from an image source or video source, or in schools where face recognition technology can be used for attendance taking. It has been observed that the accuracy and reliability of the face recognition system depends on many factors. Some of them include: the angle at which the face is facing the camera, the background noise accompanying the image source or video source, and lastly, the algorithm used for both face detection and recognition. This paper aims to evaluate the effectiveness of face recognition systems using primarily the viola-jones object detection framework for face detection and Principal Component Analysis (PCA) for face recognition. This is done by evaluating a face sample, either from an image source or from a live video source against a reliable database of faces. Thus, the reliability of the face recognition system can then be measured. Last but not least, the technique of Principal Component Analysis is compared to other face recognition techniques, specifically the Fisher Linear Discriminating (FLD) approach and the Linear Discriminant Analysis. (LDA) Bachelor of Engineering 2016-05-18T06:20:30Z 2016-05-18T06:20:30Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67584 en Nanyang Technological University 47 p. application/pdf |
| spellingShingle | DRNTU::Engineering Chua, Glen Jun Xiong Face recognition 1 |
| title | Face recognition 1 |
| title_full | Face recognition 1 |
| title_fullStr | Face recognition 1 |
| title_full_unstemmed | Face recognition 1 |
| title_short | Face recognition 1 |
| title_sort | face recognition 1 |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/67584 |
| work_keys_str_mv | AT chuaglenjunxiong facerecognition1 |