[1] W. Ali, W. Tian, S. U. Din, D. Iradukunda, and A. A. Khan, Classical and modern face recognition approaches: a complete review, vol. 80, no. 3. Multimedia Tools and Applications, 2021.
[2] A. Anand, V. Jha, and L. Sharma, “An improved local binary patterns histograms technique for face recognition for real time applications,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 7, pp. 524–529, 2019, doi: 10.35940/ijrte.B1098.0782S719.
[3] R. Bezen, Y. Edan, and I. Halachmi, “Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms,” Comput. Electron. Agric., vol. 172, no. March, p. 105345, 2020, doi: 10.1016/j.compag.2020.105345.
[4] G. Cerutti, R. Prasad, and E. Farella, “Convolutional Neural Network on Embedded Platform for People Presence Detection in Low Resolution Thermal Images,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2019-May, pp. 7610–7614, 2019, doi: 10.1109/ICASSP.2019.8682998.
[5] X. Feng, Y. Jiang, X. Yang, M. Du, and X. Li, “Computer vision algorithms and hardware implementations: A survey,” Integration, vol. 69, no. April, pp. 309–320, 2019, doi: 10.1016/j.vlsi.2019.07.005.
[6] J. L. Garcia-Arroyo and B. Garcia-Zapirain, “Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding,” Comput. Methods Programs Biomed., vol. 168, pp. 11–19, 2019, doi: 10.1016/j.cmpb.2018.11.001.
[7] A. Gupta, L. Kumar, R. Jain, and P. Nagrath, Heart Disease Prediction Using Classification (Naive Bayes), vol. 121, no. Ic4s. 2020.
[8] Y. Gurovich et al., “Identifying facial phenotypes of genetic disorders using deep learning,” Nat. Med., vol. 25, no. 1, pp. 60–64, 2019, doi: 10.1038/s41591-018-0279-0.
[9] D. K. Jain, P. Shamsolmoali, and P. Sehdev, “Extended deep neural network for facial emotion recognition,” Pattern Recognit. Lett., vol. 120, pp. 69–74, 2019, doi: 10.1016/j.patrec.2019.01.008.
[10] A. I. Khan and S. Al-Habsi, “Machine Learning in Computer Vision,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 1444–1451, 2020, doi: 10.1016/j.procs.2020.03.355.
[11] S. Nurmaini, A. Zarkasi, D. Stiawan, B. Yudho Suprapto, S. Desy Siswanti, and H. Ubaya, “Robot movement controller based on dynamic facial pattern recognition,” Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 2, p. 733, 2021, doi: 10.11591/ijeecs.v22.i2.pp733-743.
[12] M. O. Oloyede, G. P. Hancke, and H. C. Myburgh, “A review on face recognition systems: recent approaches and challenges,” Multimed. Tools Appl., vol. 79, no. 37–38, pp. 27891–27922, 2020, doi: 10.1007/s11042-020-09261-2.
[13] N. O’Mahony et al., Deep Learning vs. Traditional Computer Vision BT - Advances in Computer Vision. Springer International Publishing, 2020.
[14] K. C. Paul and S. Aslan, “An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE,” Opt. Photonics J., vol. 11, no. 04, pp. 63–78, 2021, doi: 10.4236/opj.2021.114005.
[15] F. Rubio, F. Valero, and C. Llopis-Albert, “A review of mobile robots: Concepts, methods, theoretical framework, and applications,” Int. J. Adv. Robot. Syst., vol. 16, no. 2, pp. 1–22, 2019, doi: 10.1177/1729881419839596.
[16] K. B. Schauder, W. J. Park, Y. Tsank, M. P. Eckstein, D. Tadin, and L. Bennetto, “Initial eye gaze to faces and its functional consequence on face identification abilities in autism spectrum disorder,” J. Neurodev. Disord., vol. 11, no. 1, pp. 1–20, 2019, doi: 10.1186/s11689-019-9303-z.
[17] W. J. Song, Hardware accelerator systems for embedded systems, 1st ed., vol. 122. Elsevier Inc., 2021.
[18] M. Taskiran, N. Kahraman, and C. E. Erdem, Face recognition: Past, present and future (a review), vol. 106. Elsevier Inc., 2020.
[19] S. Wan and S. Goudos, “Faster R-CNN for multi-class fruit detection using a robotic vision system,” Comput. Networks, vol. 168, p. 107036, 2020, doi: 10.1016/j.comnet.2019.107036.
[20] M. Wang and W. Deng, “Deep face recognition: A survey,” Neurocomputing, vol. 429, pp. 215–244, 2021, doi: 10.1016/j.neucom.2020.10.081.
[21] H. Yu, E. Lee, and S. B. Lee, “SymBiosis: Anti-Censorship and Anonymous Web-Browsing Ecosystem,” IEEE Access, vol. 4, pp. 3547–3556, 2016, doi: 10.1109/ACCESS.2016.2585163.
[22] G. M. Zafaruddin and H. S. Fadewar, Face recognition using eigenfaces, vol. 810. Springer Singapore, 2018.
[23] A. Zarkasi, S. Nurmaini, D. Stiawan, and B. Y. Suprapto, “Weightless Neural Networks Face Recognition Learning Process for Binary Facial Pattern,” Indones. J. Electr. Eng. Informatics, vol. 10, no. 4, pp. 955–969, 2022, doi: 10.52549/ijeei.v10i4.3957.
[24] M. Zhao, Z. Jia, Y. Cai, X. Chen, and D. Gong, “Advanced variations of two-dimensional principal component analysis for face recognition,” Neurocomputing, vol. 452, pp. 653–664, 2021, doi: 10.1016/j.neucom.2020.08.083.
[25] Q. Zhao, “Research on the application of local binary patterns based on color distance in image classification,” Multimed. Tools Appl., vol. 80, no. 18, pp. 27279–27298, 2021, doi: 10.1007/s11042-021-10996-9.
[2] A. Anand, V. Jha, and L. Sharma, “An improved local binary patterns histograms technique for face recognition for real time applications,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 7, pp. 524–529, 2019, doi: 10.35940/ijrte.B1098.0782S719.
[3] R. Bezen, Y. Edan, and I. Halachmi, “Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms,” Comput. Electron. Agric., vol. 172, no. March, p. 105345, 2020, doi: 10.1016/j.compag.2020.105345.
[4] G. Cerutti, R. Prasad, and E. Farella, “Convolutional Neural Network on Embedded Platform for People Presence Detection in Low Resolution Thermal Images,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2019-May, pp. 7610–7614, 2019, doi: 10.1109/ICASSP.2019.8682998.
[5] X. Feng, Y. Jiang, X. Yang, M. Du, and X. Li, “Computer vision algorithms and hardware implementations: A survey,” Integration, vol. 69, no. April, pp. 309–320, 2019, doi: 10.1016/j.vlsi.2019.07.005.
[6] J. L. Garcia-Arroyo and B. Garcia-Zapirain, “Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding,” Comput. Methods Programs Biomed., vol. 168, pp. 11–19, 2019, doi: 10.1016/j.cmpb.2018.11.001.
[7] A. Gupta, L. Kumar, R. Jain, and P. Nagrath, Heart Disease Prediction Using Classification (Naive Bayes), vol. 121, no. Ic4s. 2020.
[8] Y. Gurovich et al., “Identifying facial phenotypes of genetic disorders using deep learning,” Nat. Med., vol. 25, no. 1, pp. 60–64, 2019, doi: 10.1038/s41591-018-0279-0.
[9] D. K. Jain, P. Shamsolmoali, and P. Sehdev, “Extended deep neural network for facial emotion recognition,” Pattern Recognit. Lett., vol. 120, pp. 69–74, 2019, doi: 10.1016/j.patrec.2019.01.008.
[10] A. I. Khan and S. Al-Habsi, “Machine Learning in Computer Vision,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 1444–1451, 2020, doi: 10.1016/j.procs.2020.03.355.
[11] S. Nurmaini, A. Zarkasi, D. Stiawan, B. Yudho Suprapto, S. Desy Siswanti, and H. Ubaya, “Robot movement controller based on dynamic facial pattern recognition,” Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 2, p. 733, 2021, doi: 10.11591/ijeecs.v22.i2.pp733-743.
[12] M. O. Oloyede, G. P. Hancke, and H. C. Myburgh, “A review on face recognition systems: recent approaches and challenges,” Multimed. Tools Appl., vol. 79, no. 37–38, pp. 27891–27922, 2020, doi: 10.1007/s11042-020-09261-2.
[13] N. O’Mahony et al., Deep Learning vs. Traditional Computer Vision BT - Advances in Computer Vision. Springer International Publishing, 2020.
[14] K. C. Paul and S. Aslan, “An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE,” Opt. Photonics J., vol. 11, no. 04, pp. 63–78, 2021, doi: 10.4236/opj.2021.114005.
[15] F. Rubio, F. Valero, and C. Llopis-Albert, “A review of mobile robots: Concepts, methods, theoretical framework, and applications,” Int. J. Adv. Robot. Syst., vol. 16, no. 2, pp. 1–22, 2019, doi: 10.1177/1729881419839596.
[16] K. B. Schauder, W. J. Park, Y. Tsank, M. P. Eckstein, D. Tadin, and L. Bennetto, “Initial eye gaze to faces and its functional consequence on face identification abilities in autism spectrum disorder,” J. Neurodev. Disord., vol. 11, no. 1, pp. 1–20, 2019, doi: 10.1186/s11689-019-9303-z.
[17] W. J. Song, Hardware accelerator systems for embedded systems, 1st ed., vol. 122. Elsevier Inc., 2021.
[18] M. Taskiran, N. Kahraman, and C. E. Erdem, Face recognition: Past, present and future (a review), vol. 106. Elsevier Inc., 2020.
[19] S. Wan and S. Goudos, “Faster R-CNN for multi-class fruit detection using a robotic vision system,” Comput. Networks, vol. 168, p. 107036, 2020, doi: 10.1016/j.comnet.2019.107036.
[20] M. Wang and W. Deng, “Deep face recognition: A survey,” Neurocomputing, vol. 429, pp. 215–244, 2021, doi: 10.1016/j.neucom.2020.10.081.
[21] H. Yu, E. Lee, and S. B. Lee, “SymBiosis: Anti-Censorship and Anonymous Web-Browsing Ecosystem,” IEEE Access, vol. 4, pp. 3547–3556, 2016, doi: 10.1109/ACCESS.2016.2585163.
[22] G. M. Zafaruddin and H. S. Fadewar, Face recognition using eigenfaces, vol. 810. Springer Singapore, 2018.
[23] A. Zarkasi, S. Nurmaini, D. Stiawan, and B. Y. Suprapto, “Weightless Neural Networks Face Recognition Learning Process for Binary Facial Pattern,” Indones. J. Electr. Eng. Informatics, vol. 10, no. 4, pp. 955–969, 2022, doi: 10.52549/ijeei.v10i4.3957.
[24] M. Zhao, Z. Jia, Y. Cai, X. Chen, and D. Gong, “Advanced variations of two-dimensional principal component analysis for face recognition,” Neurocomputing, vol. 452, pp. 653–664, 2021, doi: 10.1016/j.neucom.2020.08.083.
[25] Q. Zhao, “Research on the application of local binary patterns based on color distance in image classification,” Multimed. Tools Appl., vol. 80, no. 18, pp. 27279–27298, 2021, doi: 10.1007/s11042-021-10996-9.
- Abstract viewed - 189 times
- PDF downloaded - 131 times
Affiliations
Ahmad Zarkasi
Universitas Sriwijaya
Huda Ubaya
Universitas Sriwijaya
Kemahyanto Exaudi
Universitas Sriwijaya
Alif Almuqsit
Universitas Sriwijaya
Osvari Arsalan
Universitas Sriwijaya
Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method
Abstract
The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.