Robot movement controller based on dynamic facial pattern recognition
In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by c...
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| Main Authors: | , , , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-05-01.
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| Online Access: | Get fulltext |
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| LEADER | 02870 am a22003493u 4500 | ||
|---|---|---|---|
| 001 | ijeecs23940_14957 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Nurmaini, Siti |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Zarkasi, Ahmad |e author |
| 700 | 1 | 0 | |a Stiawan, Deris |e author |
| 700 | 1 | 0 | |a Yudho Suprapto, Bhakti |e author |
| 700 | 1 | 0 | |a Desy Siswanti, Sri |e author |
| 700 | 1 | 0 | |a Ubaya, Huda |e author |
| 245 | 0 | 0 | |a Robot movement controller based on dynamic facial pattern recognition |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-05-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23940 | ||
| 520 | |a In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms). | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Face detection; Mobile robots; Navigation technique; Ram based neural network; Ram discriminator | ||
| 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 22, No 2: May 2021; 733-743 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23940/14957 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23940/14957 |z Get fulltext |