Analysis of Different M-Band Wavelet Filters for Face Recognition using Nearest Neighbor Classifier
Face recognition system is one of the most interesting studied topics in computer vision for past two decades. Among the other popular biometrics such as the retina, fingerprint, and iris recognition systems, the face recognition is capable of recognizing the uncooperative samples in a non-intrusive...
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| Main Authors: | , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2018-11-01.
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| Online Access: | Get fulltext |
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| LEADER | 02245 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs12946_9522 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Hemalatha, C |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Logashanmugam, E |e author |
| 245 | 0 | 0 | |a Analysis of Different M-Band Wavelet Filters for Face Recognition using Nearest Neighbor Classifier |
| 260 | |b Institute of Advanced Engineering and Science, |c 2018-11-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12946 | ||
| 520 | |a Face recognition system is one of the most interesting studied topics in computer vision for past two decades. Among the other popular biometrics such as the retina, fingerprint, and iris recognition systems, the face recognition is capable of recognizing the uncooperative samples in a non-intrusive manner. Also, it can be applied to many applications of surveillance security, forensics, border control, digital entertainment where face recognition is used in most. In the proposed system an automatic face recognition system is discussed. The proposed recognition system is based on the Dual-Tree M-Band Wavelet Transform (DTMBWT) transform algorithm and features obtained by varying the different filter in the DTMBWT transform. Then the different filter features are classified by means of the K-Nearest Neighbor (KNN) classifier for recognizing the face correctly. The implementation of the system is done by using the ORL face image database, and the performance metrics are calculated. | ||
| 540 | |a Copyright (c) 2018 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Face recognition; DTMBWT; M-band; KNN; ORL database. | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 12, No 2: November 2018; 824-831 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v12.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12946/9522 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12946/9522 |z Get fulltext |