EKSTRAKSI CIRI GEOMETRIS UNTUK PENGENALAN WAJAH
Face is part of the human body that was first used to identify a person. Currently, the application of face recognition has been used for a variety of fields. The main problem in face recognition is to find a feature that is really appropriate and efficient. This study tried to examine the most accu...
| Main Authors: | , |
|---|---|
| Format: | Thesis |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2012
|
| Subjects: |
| _version_ | 1797032736665370624 |
|---|---|
| author | , KHOLISTIANINGSIH , Dr. Eng Ir. Risanuri Hidayat, M.Sc., |
| author_facet | , KHOLISTIANINGSIH , Dr. Eng Ir. Risanuri Hidayat, M.Sc., |
| author_sort | , KHOLISTIANINGSIH |
| collection | UGM |
| description | Face is part of the human body that was first used to identify a person.
Currently, the application of face recognition has been used for a variety of fields.
The main problem in face recognition is to find a feature that is really appropriate
and efficient.
This study tried to examine the most accurate geometric features. The best
feature is a feature that may characterize a person with the least amount. The
method is measurement the distance between the components of the face. The
measurement results are normalized with the sum of the distances.
Based on the research result, four geometric features are able to recognize
someone's face. These features are the distance between the inner tip of the left
eye to right tip of nose, the distance between center of nose to the center of lip, the
distance between the outer tip of the right eye to left tip of nose and the length of
nose. Test results show that geometric features can be used for face recognition
applications as well. These features can recognize a face that�s facing forward.
Smilling expressions did not significantly affect the accuracy of the feature.
However, geometric features are sensitive for the changing of the position of the
slope face, so it must be less than 11 °. Face recognition rate are 100% for
normal face expression, 100% for smiling face expression, and 50% for slope
face. The least of false error rate value is 7,5% for combination of 9 features |
| first_indexed | 2024-03-13T22:43:03Z |
| format | Thesis |
| id | oai:generic.eprints.org:100572 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T22:43:03Z |
| publishDate | 2012 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:1005722016-03-04T08:47:20Z https://repository.ugm.ac.id/100572/ EKSTRAKSI CIRI GEOMETRIS UNTUK PENGENALAN WAJAH , KHOLISTIANINGSIH , Dr. Eng Ir. Risanuri Hidayat, M.Sc., ETD Face is part of the human body that was first used to identify a person. Currently, the application of face recognition has been used for a variety of fields. The main problem in face recognition is to find a feature that is really appropriate and efficient. This study tried to examine the most accurate geometric features. The best feature is a feature that may characterize a person with the least amount. The method is measurement the distance between the components of the face. The measurement results are normalized with the sum of the distances. Based on the research result, four geometric features are able to recognize someone's face. These features are the distance between the inner tip of the left eye to right tip of nose, the distance between center of nose to the center of lip, the distance between the outer tip of the right eye to left tip of nose and the length of nose. Test results show that geometric features can be used for face recognition applications as well. These features can recognize a face that�s facing forward. Smilling expressions did not significantly affect the accuracy of the feature. However, geometric features are sensitive for the changing of the position of the slope face, so it must be less than 11 °. Face recognition rate are 100% for normal face expression, 100% for smiling face expression, and 50% for slope face. The least of false error rate value is 7,5% for combination of 9 features [Yogyakarta] : Universitas Gadjah Mada 2012 Thesis NonPeerReviewed , KHOLISTIANINGSIH and , Dr. Eng Ir. Risanuri Hidayat, M.Sc., (2012) EKSTRAKSI CIRI GEOMETRIS UNTUK PENGENALAN WAJAH. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=57098 |
| spellingShingle | ETD , KHOLISTIANINGSIH , Dr. Eng Ir. Risanuri Hidayat, M.Sc., EKSTRAKSI CIRI GEOMETRIS UNTUK PENGENALAN WAJAH |
| title | EKSTRAKSI CIRI GEOMETRIS
UNTUK PENGENALAN WAJAH |
| title_full | EKSTRAKSI CIRI GEOMETRIS
UNTUK PENGENALAN WAJAH |
| title_fullStr | EKSTRAKSI CIRI GEOMETRIS
UNTUK PENGENALAN WAJAH |
| title_full_unstemmed | EKSTRAKSI CIRI GEOMETRIS
UNTUK PENGENALAN WAJAH |
| title_short | EKSTRAKSI CIRI GEOMETRIS
UNTUK PENGENALAN WAJAH |
| title_sort | ekstraksi ciri geometris untuk pengenalan wajah |
| topic | ETD |
| work_keys_str_mv | AT kholistianingsih ekstraksicirigeometrisuntukpengenalanwajah AT drengirrisanurihidayatmsc ekstraksicirigeometrisuntukpengenalanwajah |