�PENGENALAN WAJAH DENGAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA) PADA SISTEM ABSENSI REAL-TIME
In information technology, biometrics system is a system that refers to technologies for measuring and analyzing human body characteristics. body parts such as fingerprints, retina, eye, voice patterns and facial patterns, are examples of body parts that can be used to process otenfikasi. Facial rec...
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| Main Authors: | , |
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| 格式: | Theses and Dissertations NonPeerReviewed |
| 出版: |
[Yogyakarta] : Universitas Gadjah Mada
2012
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| 主題: | |
| 在線閱讀: | https://repository.ugm.ac.id/100583/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=57040 |
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| 總結: | In information technology, biometrics system is a system that refers to
technologies for measuring and analyzing human body characteristics. body parts
such as fingerprints, retina, eye, voice patterns and facial patterns, are examples
of body parts that can be used to process otenfikasi. Facial recognition
technology increasingly used and developed with a very wide range of
applications, such as security systems, access control systems area, and student
attendance system.
This research presents a biometrics attendance system that applies the
identification of facial recognition techniques. The system is focused on the
design of attendance system that works by detecting faces in real-time, natural,
dynamic and automatic without any manual process, with the aim that students do
not know the ongoing process of absenteeism, it should not interfere with the
learning process. The system is implemented on a lecture at the university.
This system has two stages of the process of absenteeism, the first process
of detecting every face that passes through the entrance of the room to get a facial
image, the second is to do facial recognition on the face image is detected to
determine the presence. The system uses an IP-Camera to detect the faces of
students.
These systems apply the Viola-Jones algorithm for face detection
technique and the method of Principal Component Analysis (PCA) for face
recognition techniques to obtain eigenface from each facial image.
The results from this research suggest the accuracy rate of 79% of face
recognition. These results were obtained by performing 100 trials of 100 facial
images consisting of 10 students and each student has 10 face images. |
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