Kusnadi, Adhi and Wella, Wella and Pane, Ivransa Zuhdi (2019) Removing DCT High Frequency on Feature Detector Repeatability Quality. 2019 5th International Conference on New Media Studies (CONMEDIA).
Full text not available from this repository.Abstract
This study evaluates five feature detectors popular used in 3-dimensional face recognition systems, where face images quality is improved by removing high frequencies at the Discrete Cosine Transform coefficient domain, because at this frequency contained noise. The evaluation is important to be done because the feature detector functions to look for keypoints on the face image needed in 3-dimensional face reconstruction from 2 dimensional face images, and th e accuracy of 3d face recognition depends on the success of the reconstruction. Several researches had been undertaken in 3D face recognition, among others by implementing feature detector to determine the same key point on two face images 2 dimensional at different angles to simulate epipolar geometry. However, the implementation of feature detector does not result in a maximum value of reliability, and thus improvements should be made, it could be on feature extraction and on feature selection stages. Discrete Cosine Transform algorithm is used as a feature extractor, which produced a coefficient consisting of low frequency, middle frequency and high frequency bands. A test of five feature extractors was carried out by removing the high frequency band from the coefficient as these frequencies. The face images were taken from ORL database and Head Pose Image Database, then data before and after the high frequency bands were removed are compared. SURF attained the highest F Score for both conditions, and on average, the value F score increases from the condition before the high frequency is removed and after it is removed from the DCT coefficient.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 004 Computer Science, Data Processing, Hardware 300 Social Sciences > 310 Statistics |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 05 Oct 2021 04:13 |
| Last Modified: | 14 Jan 2022 06:46 |
| URI: | https://kc.umn.ac.id/id/eprint/18500 |
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