[1]J. Barbosa, W. K. Seo, and J. Kang, “ParaFaceTest: An ensemble of regression tree-based facial features extraction for efficient facial paralysis classification,” BMC Med. Imaging, vol. 19, no. 1, p. 30, Apr. 2019, doi: 10.1186/s12880-019-0330-8.
[2]V. Maruthapillai and M. Murugappan, “Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions,” PLoS One, vol. 11, no. 2, p. e0149003, Feb. 2016, doi: 10.1371/journal.pone.0149003
[3]R. Wang, “Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition,” Sci. World J., vol. 2015, 2015, doi: 10.1155/2015/842084.
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[5]D. Jia, J. Cao, W. D. Song, X. L. Tang,and H. Zhu, “Colour FAST (CFAST) match: Fast affine template matching for colour images,” Electron. Lett., vol. 52, no. 14, pp. 1220–1221, Jul. 2016, doi: 10.1049/el.2016.1331.
[6]Z. Liu, Y. Guo, Z. Feng, and S. Zhang, “Improved Rectangle Template Matching Based Feature Point Matching Algorithm,” in Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019, 2019, pp. 2275–2280, doi: 10.1109/CCDC.2019.8833208.
[7]H. Liu and L. Wang, “Gesture recognition for human-robot collaboration: A review,” Int. J. Ind. Ergon., vol. 68, pp. 355–367, Nov. 2018, doi: 10.1016/j.ergon.2017.02.004.
[8]D. Jia, J. Cao, W. D. Song, X. L. Tang, and H. Zhu, “Colour FAST (CFAST) match: Fast affine template matching for colour images,” Electron. Lett., vol. 52, no. 14, pp. 1220–1221, Jul. 2016, doi: 10.1049/el.2016.1331.
[9]P. Tome, R. Vera-Rodriguez, J. Fierrez, and J. Ortega-Garcia, “Facial soft biometric features for forensic face recognition,” Forensic Sci. Int., vol. 257, pp. 271–284, Dec. 2015, doi: 10.1016/j.forsciint.2015.09.002.
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[14]C. Peng, W. Bu, J. Xiao, K. Wong, and M. Yang, “An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance,” Appl. Sci.,
vol. 8, no. 11, p. 2222, Nov. 2018, doi: 10.3390/app8112222.
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[20]D. Chen, Q. Chen, J. Wu, X. Yu, and T. Jia, “Face swapping: Realistic image synthesis based on facial landmarks alignment,” Math. Probl. Eng., vol. 2019, pp. 1–12, 2019, doi: 10.1155/2019/8902701
[21]F. Fuentes-Hurtado, J. A. Diego-Mas, V. Naranjo, and M.Alcañiz, “Automatic classification of human facial features based on their appearance,” PLoS One, vol. 14, no. 1, p. e0211314, Jan. 2019, doi: 10.1371/journal.pone.0211314.
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[23]H. A. Shehu, W. Browne, and H. Eisenbarth, “An Adversarial Attacks Resistance-based Approach to Emotion Recognition from Images using Facial Landmarks,” 29th IEEE Int. Conf. Robot Hum. Interact. Commun. RO-MAN 2020, pp. 1307–1314, 2020, doi: 10.1109/RO-MAN47096.2020.9223510.
[24]Z. Zhang, D. Lian, and S. Gao, “RGB-D-based gaze point estimation via multi-column CNNs and facial landmarks global optimization,” Vis. Comput., vol. 37, no. 7, pp. 1731–1741, 2021, doi: 10.1007/s00371-020-01934-1.
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[27]SCAD Institute of Technology and Institute of Electrical and Electronics Engineers, “‘A Study of LBPH, Eigenface, Fisherface and Haar-like features for Face recognition using OpenCV’ ICISS-2019 : proceedings of the International Conference on Intelligent Sustainable Systems (ICISS 2019): 21-22, February 2019,” 2019 Int. Conf. Intell. Sustain. Syst., pp. 219–224, 2019.
[28]C. Y. Low, A. B. J. Teoh, and C. J. Ng, “Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 1, pp. 115–129, Jan. 2019, doi: 10.1109/TCSVT.2017.2761829
[29]A. Ranftl, F. Alonso-Fernandez, S. Karlsson, and J. Bigun, “Real-time AdaBoost cascade face tracker based on likelihood map and optical flow,” IET Biometrics, vol. 6, no. 6, pp. 468–477, Nov. 2017, doi: 10.1049/iet-bmt.2016.0202.
[30]M. M. Ahsan, Y. Li, J. Zhang, M. T. Ahad, and K. D. Gupta, “Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions,” Technologies, vol. 9, no. 2, p. 31, 2021, doi: 10.3390/technologies9020031.
[31]B. W. Yohanes, R. Diaz Airlangga, and I. Setyawan, “Real Time Face Recognition Comparison Using Fisherfaces and Local Binary Pattern,” Proc. -2018 4th Int. Conf. Sci. Technol. ICST 2018, vol. 1, pp. 1–5, 2018, doi: 10.1109/ICSTC.2018.8528608.
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[33]S. Karanwal and M. Diwakar, “OD-LBP: Orthogonal difference-local binary pattern for Face Recognition,” Digit. Signal Process. A Rev. J., vol. 110, p. 102948, 2021, doi: 10.1016/j.dsp.2020.102948.
[34]L. Li, X. Feng, Z. Xia, X. Jiang, and A. Hadid, “Face spoofing detection with local binary pattern network,” J. Vis. Commun. Image Represent., vol. 54, pp. 182–192, 2018, doi: 10.1016/j.jvcir.2018.05.009.
[35]Y. H. Chan, Y. Z. Zeng, H. C. Wu, M. C. Wu, and H. M. Sun, “Effective pneumothorax detection for chest X-ray images using local binary pattern and support vector machine,” J. Healthc. Eng., vol. 2018, 2018, doi: 10.1155/2018/2908517.
[36]M. De Gregorio and M. Giordano, “Background estimation by weightless neural networks,” Pattern Recognit. Lett., vol. 96, pp. 55–65, Sep. 2017, doi: 10.1016/j.patrec.2017.05.029.
[37] M. Berger, A. F. De Souza, J. de O. Neto, E. de Aguiar, and T. Oliveira-Santos, “Visual tracking with VG-RAM Weightless Neural Networks,” Neurocomputing, vol. 183, pp. 90–105, Mar. 2016, doi: 10.1016/j.neucom.2015.04.127.
[38]D. O. Cardoso, F. França, and J. Gama, “A bounded neural network for open set recognition,” in Proceedings of the International Joint Conference on Neural Networks, 2015, vol. 2015-September, pp. 1–7, doi: 10.1109/IJCNN.2015.7280680.
[39]D. O. Cardoso, J. Gama, and F. M. G. França, “Weightless neural networks for open set recognition,” Mach. Learn., vol. 106, no. 9–10, pp. 1547–1567, Oct. 2017, doi: 10.1007/s10994-017-5646-4.
[40]A. Kaczmarczyk and W. Zatorska, “Accelerating Image Fusion Algorithms Using CUDA on Embedded Industrial Platforms Dedicated to UAV and UGV,” in Advances in Intelligent Systems and Computing, 2020, vol. 920, pp. 697–706, doi: 10.1007/978-3-030-13273-6_65.
[41]Z. Shi, H. Yu, and L. Kong, “Image stitching algorithm based on embedded system,” in Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018, 2018, pp. 1174–1178, doi: 10.1109/ICMA.2018.8484530.
[42]S. Nurmaini and A. Zarkasi, “Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots,” Artic. J. Inf. Process. Syst., 2015, doi: 10.3745/JIPS.01.0008.
[43]C. Qin, W. Zhang, F. Cao, X. Zhang, and C. C. Chang, “Separablereversible data hiding in encrypted images via adaptive embedding strategy with block selection,” Signal Processing, vol. 153, pp. 109–122, Dec. 2018, doi: 10.1016/j.sigpro.2018.07.008.
[44]G. Fumera and F. Roli, “Support vector machines with embedded reject option,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2388, pp. 68–82, doi: 10.1007/3-540-45665-1_6.
[45]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.
[2]V. Maruthapillai and M. Murugappan, “Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions,” PLoS One, vol. 11, no. 2, p. e0149003, Feb. 2016, doi: 10.1371/journal.pone.0149003
[3]R. Wang, “Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition,” Sci. World J., vol. 2015, 2015, doi: 10.1155/2015/842084.
[4]A. Zarkasi et al., “Face Movement Detection Using Template Matching,” in Proceedings of 2018 International Conference on Electrical Engineering and Computer Science, ICECOS 2018, 2019, pp. 333–338, doi: 10.1109/ICECOS.2018.8605215.
[5]D. Jia, J. Cao, W. D. Song, X. L. Tang,and H. Zhu, “Colour FAST (CFAST) match: Fast affine template matching for colour images,” Electron. Lett., vol. 52, no. 14, pp. 1220–1221, Jul. 2016, doi: 10.1049/el.2016.1331.
[6]Z. Liu, Y. Guo, Z. Feng, and S. Zhang, “Improved Rectangle Template Matching Based Feature Point Matching Algorithm,” in Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019, 2019, pp. 2275–2280, doi: 10.1109/CCDC.2019.8833208.
[7]H. Liu and L. Wang, “Gesture recognition for human-robot collaboration: A review,” Int. J. Ind. Ergon., vol. 68, pp. 355–367, Nov. 2018, doi: 10.1016/j.ergon.2017.02.004.
[8]D. Jia, J. Cao, W. D. Song, X. L. Tang, and H. Zhu, “Colour FAST (CFAST) match: Fast affine template matching for colour images,” Electron. Lett., vol. 52, no. 14, pp. 1220–1221, Jul. 2016, doi: 10.1049/el.2016.1331.
[9]P. Tome, R. Vera-Rodriguez, J. Fierrez, and J. Ortega-Garcia, “Facial soft biometric features for forensic face recognition,” Forensic Sci. Int., vol. 257, pp. 271–284, Dec. 2015, doi: 10.1016/j.forsciint.2015.09.002.
[10]M. Nordt and S. Weigelt, “Face recognition is similarly affected by viewpoint in school-aged children and adults,” PeerJ, vol. 2017, no. 5, p. e3253, May 2017, doi: 10.7717/peerj.3253.
[11]C. Bi et al., “Supervised filter learning for representation basedface recognition,” PLoS One, vol. 11, no. 7, p. e0159084, Jul. 2016, doi: 10.1371/journal.pone.0159084.
[12]L. Sun, X. Liang, and Q. Zhao, “Recursive templates segmentation and exemplars matching for human parsing,” Comput. J., vol. 57, no. 3, pp. 364–377, Mar. 2014, doi: 10.1093/comjnl/bxt100.
[13]H. Hosoya and A. Hyvärinen, “A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing,” PLoS Comput. Biol., vol. 13, no. 7, p. e1005667, Jul. 2017,doi: 10.1371/journal.pcbi.1005667
[14]C. Peng, W. Bu, J. Xiao, K. Wong, and M. Yang, “An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance,” Appl. Sci.,
vol. 8, no. 11, p. 2222, Nov. 2018, doi: 10.3390/app8112222.
[15]J. Li and F. Yang, “Research on multi-robot scheduling algorithms based on machine vision,” Eurasip J. Image Video Process., vol. 2018, no. 1, 2018, doi: 10.1186/s13640-018-0355-x.
[16]T. M. Murphy, R. Broussard, R. Schultz, R. Rakvic, and H. Ngo, “Face detection with a Viola-Jones based hybrid network,” IET Biometrics, vol. 6, no. 3, pp. 200–210, May 2017, doi: 10.1049/iet-bmt.2016.0037.
[17]M. Kasar, B. Vidyapeeth, M. M. Kasar, D. Bhattacharyya, and T.-H. Kim, “Face Recognition Using Neural Network: A Review Analysis of Multi-View Face Images and Recognition Through ANN View project Face Recognition Using Neural Network: A Review,” Artic. Int. J. Secur. its Appl., vol. 10, no. 3, pp. 81–100, 2016, doi: 10.14257/ijsia.2016.10.3.08.
[18]M. Cummaudo et al., “Pitfalls at the root of facial assessment on photographs: A quantitative study of accuracy in positioning facial landmarks,” Int. J. Legal Med., vol. 127, no. 3, pp. 699–706, 2013, doi: 10.1007/s00414-013-0850-7.
[19]R. H, “Real Time Driver Drowsiness Detection System using OpenCV,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. VI, pp. 3254–3260, 2021, doi: 10.22214/ijraset.2021.35811.
[20]D. Chen, Q. Chen, J. Wu, X. Yu, and T. Jia, “Face swapping: Realistic image synthesis based on facial landmarks alignment,” Math. Probl. Eng., vol. 2019, pp. 1–12, 2019, doi: 10.1155/2019/8902701
[21]F. Fuentes-Hurtado, J. A. Diego-Mas, V. Naranjo, and M.Alcañiz, “Automatic classification of human facial features based on their appearance,” PLoS One, vol. 14, no. 1, p. e0211314, Jan. 2019, doi: 10.1371/journal.pone.0211314.
[22]H. Kim, H. W. Kim, and E. Hwang,“Real-time shape tracking of facial landmarks,” Multimed. Tools Appl., vol. 79, no. 23–24, pp. 15945–15963, 2020, doi: 10.1007/s11042-018-6814-7.
[23]H. A. Shehu, W. Browne, and H. Eisenbarth, “An Adversarial Attacks Resistance-based Approach to Emotion Recognition from Images using Facial Landmarks,” 29th IEEE Int. Conf. Robot Hum. Interact. Commun. RO-MAN 2020, pp. 1307–1314, 2020, doi: 10.1109/RO-MAN47096.2020.9223510.
[24]Z. Zhang, D. Lian, and S. Gao, “RGB-D-based gaze point estimation via multi-column CNNs and facial landmarks global optimization,” Vis. Comput., vol. 37, no. 7, pp. 1731–1741, 2021, doi: 10.1007/s00371-020-01934-1.
[25]M. Anggo and La Arapu, “Face Recognition Using Fisherface Method,” J. Phys. Conf. Ser., vol. 1028, no. 1, 2018, doi: 10.1088/1742-6596/1028/1/012119.
[26] X. Y. Jing, H. S. Wong, and D. Zhang, “Face recognition based on 2D Fisherface approach,” Pattern Recognit., vol. 39, no. 4, pp. 707–710, 2006, doi: 10.1016/j.patcog.2005.10.020.
[27]SCAD Institute of Technology and Institute of Electrical and Electronics Engineers, “‘A Study of LBPH, Eigenface, Fisherface and Haar-like features for Face recognition using OpenCV’ ICISS-2019 : proceedings of the International Conference on Intelligent Sustainable Systems (ICISS 2019): 21-22, February 2019,” 2019 Int. Conf. Intell. Sustain. Syst., pp. 219–224, 2019.
[28]C. Y. Low, A. B. J. Teoh, and C. J. Ng, “Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 1, pp. 115–129, Jan. 2019, doi: 10.1109/TCSVT.2017.2761829
[29]A. Ranftl, F. Alonso-Fernandez, S. Karlsson, and J. Bigun, “Real-time AdaBoost cascade face tracker based on likelihood map and optical flow,” IET Biometrics, vol. 6, no. 6, pp. 468–477, Nov. 2017, doi: 10.1049/iet-bmt.2016.0202.
[30]M. M. Ahsan, Y. Li, J. Zhang, M. T. Ahad, and K. D. Gupta, “Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions,” Technologies, vol. 9, no. 2, p. 31, 2021, doi: 10.3390/technologies9020031.
[31]B. W. Yohanes, R. Diaz Airlangga, and I. Setyawan, “Real Time Face Recognition Comparison Using Fisherfaces and Local Binary Pattern,” Proc. -2018 4th Int. Conf. Sci. Technol. ICST 2018, vol. 1, pp. 1–5, 2018, doi: 10.1109/ICSTC.2018.8528608.
[32]M. Kalakech, A. Porebski, N. Vandenbroucke, and D. Hamad, “Unsupervised local binary pattern histogram selection scores for color texture classification,” J. Imaging, vol. 4, no. 10, 2018, doi: 10.3390/jimaging4100112.
[33]S. Karanwal and M. Diwakar, “OD-LBP: Orthogonal difference-local binary pattern for Face Recognition,” Digit. Signal Process. A Rev. J., vol. 110, p. 102948, 2021, doi: 10.1016/j.dsp.2020.102948.
[34]L. Li, X. Feng, Z. Xia, X. Jiang, and A. Hadid, “Face spoofing detection with local binary pattern network,” J. Vis. Commun. Image Represent., vol. 54, pp. 182–192, 2018, doi: 10.1016/j.jvcir.2018.05.009.
[35]Y. H. Chan, Y. Z. Zeng, H. C. Wu, M. C. Wu, and H. M. Sun, “Effective pneumothorax detection for chest X-ray images using local binary pattern and support vector machine,” J. Healthc. Eng., vol. 2018, 2018, doi: 10.1155/2018/2908517.
[36]M. De Gregorio and M. Giordano, “Background estimation by weightless neural networks,” Pattern Recognit. Lett., vol. 96, pp. 55–65, Sep. 2017, doi: 10.1016/j.patrec.2017.05.029.
[37] M. Berger, A. F. De Souza, J. de O. Neto, E. de Aguiar, and T. Oliveira-Santos, “Visual tracking with VG-RAM Weightless Neural Networks,” Neurocomputing, vol. 183, pp. 90–105, Mar. 2016, doi: 10.1016/j.neucom.2015.04.127.
[38]D. O. Cardoso, F. França, and J. Gama, “A bounded neural network for open set recognition,” in Proceedings of the International Joint Conference on Neural Networks, 2015, vol. 2015-September, pp. 1–7, doi: 10.1109/IJCNN.2015.7280680.
[39]D. O. Cardoso, J. Gama, and F. M. G. França, “Weightless neural networks for open set recognition,” Mach. Learn., vol. 106, no. 9–10, pp. 1547–1567, Oct. 2017, doi: 10.1007/s10994-017-5646-4.
[40]A. Kaczmarczyk and W. Zatorska, “Accelerating Image Fusion Algorithms Using CUDA on Embedded Industrial Platforms Dedicated to UAV and UGV,” in Advances in Intelligent Systems and Computing, 2020, vol. 920, pp. 697–706, doi: 10.1007/978-3-030-13273-6_65.
[41]Z. Shi, H. Yu, and L. Kong, “Image stitching algorithm based on embedded system,” in Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018, 2018, pp. 1174–1178, doi: 10.1109/ICMA.2018.8484530.
[42]S. Nurmaini and A. Zarkasi, “Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots,” Artic. J. Inf. Process. Syst., 2015, doi: 10.3745/JIPS.01.0008.
[43]C. Qin, W. Zhang, F. Cao, X. Zhang, and C. C. Chang, “Separablereversible data hiding in encrypted images via adaptive embedding strategy with block selection,” Signal Processing, vol. 153, pp. 109–122, Dec. 2018, doi: 10.1016/j.sigpro.2018.07.008.
[44]G. Fumera and F. Roli, “Support vector machines with embedded reject option,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2388, pp. 68–82, doi: 10.1007/3-540-45665-1_6.
[45]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.
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Affiliations
Ahmad Zarkasi
Universitas Sriwijaya
Siti Nurmaini
Affiliation not stated
Deris Stiawan
Affiliation not stated
Bhakti Yudho Suprapto
Affiliation not stated
Huda Ubaya
Affiliation not stated
Rizki Kurniati
Affiliation not stated
Performance Comparison of Feature Face Detection Algorithm on The Embedded Platform
Abstract
The intensity of light will greatly affect every process carried out in image processing, especially facial images. It is important to analyze how the performance of each face detection method when tested at several lighting levels. In face detection, various methods can be used and have been tested. The FLP method automates the identification of the location of facial points. The Fisherface method reduces the dimensions obtained from PCA calculations. The LBPH method converts the texture of a face image into a binary value, while the WNNs method uses RAM to process image data, using the WiSARD architecture. This study proposes a technique for testing the effect of light on the performance of face detection methods, on an embedded platform. The highest accuracy was achieved by the LBPH and WNNs methods with an accuracy value of 98% at a lighting level of 400 lx. Meanwhile, at the lowest lighting level of 175 lx, all methods have a fairly good level of accuracy, which is between 75% to 83%.