• Helfy Susilawati Teknik Elektro, Fakultas Teknik, Universitas Garut, Indonesia
  • Ade Rukmana Teknik Elektro, Fakultas Teknik, Universitas Garut, Indonesia
  • Fitri Nuraeni Teknik Informatika, Fakultas Teknik, Institut Teknologi Garut, Indonesia
Keywords: Face recognition, Hand gestures, LBPH


Face detection is currently used for various purposes, one of which is to record employees attendance. This strategy is ineffective since the employees still can hack the attendance by making their own photos and put them in their desks. If they are unable to come to the office,they can always ask their colleagues to submit their already available photos.Therefore, an alternative that can complement the current face detection method is highly needed.One of the methods that can be used is hand gesture detection.This study aims to detect hand gestures made by the employees to ensure whether they really come to work or not,so the chance for manipulation is quite small.For the purpose of hand gesture recognition, this study utilized Local Binary Pattern Histogram algorithm. LBPH is an algorithm used for the image matching process between images that have been given training and images taken in real time.The hand gesture image was first taken using a raspberry pi camera and then processed to examine whether it matches the registered ID or not.The results showed that ID recognition by using hand gestures is detectable and is in accordance with the registered ID.The number recognition in hand gestures includes numbers 1 to 10. The test results showed that, the average time required for reading hand gestures using a laptop was 9.2 seconds, while that of using raspberry was 14,2 seconds.Motion reading using a raspberry takes longer than that of using a laptop because the laptop's performance is higher than that of a raspberry.


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R. E. Nogales and M. E. Benalcázar, “Hand gesture recognition using machine learning and infrared information: a systematic literature review,” Int. J. Mach. Learn. Cybern., vol. 12, no. 10, 2021, doi: 10.1007/s13042-021-01372-y.

A. N. Aziz and A. Kurniawardhani, “The Development of Hand Gestures Recognition Research: A Review,” Int. J. Artif. Intell. Res., vol. 6, no. 1, 2022.

M. J. Cheok, Z. Omar, and M. H. Jaward, “A review of hand gesture and sign language recognition techniques,” Int. J. Mach. Learn. Cybern., vol. 10, no. 1, 2019, doi: 10.1007/s13042-017-0705-5.

M. Oudah, A. Al-Naji, and J. Chahl, “Hand Gesture Recognition Based on Computer Vision: A Review of Techniques,” Journal of Imaging, vol. 6, no. 8. 2020, doi: 10.3390/JIMAGING6080073.

D. Sarma and M. K. Bhuyan, “Methods, Databases and Recent Advancement of Vision-Based Hand Gesture Recognition for HCI Systems: A Review,” SN Computer Science, vol. 2, no. 6. 2021, doi: 10.1007/s42979-021-00827-x.

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, 2019, doi: 10.35940/ijrte.B1098.0782S719.

F. Deeba, A. Ahmed, H. Memon, F. A. Dharejo, and A. Ghaffar, “LBPH-based enhanced real-time face recognition,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 5, 2019, doi: 10.14569/ijacsa.2019.0100535.

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, 2021, doi: 10.3390/technologies9020031.

O. Mujahid and Z. Ullah, “High Speed Partial Pattern Classification System Using a CAM-Based LBP Histogram on FPGA,” IEEE Embed. Syst. Lett., vol. 12, no. 3, pp. 87–90, 2020, doi: 10.1109/LES.2019.2956154.

M. S. Kaushik and A. B. Kandali, “Recognition of facial expressions extracting salient features using local binary patterns and histogram of oriented gradients,” 2017 Int. Conf. Energy, Commun. Data Anal. Soft Comput. ICECDS 2017, pp. 1201–1205, 2018, doi: 10.1109/ICECDS.2017.8389632.

T. Adep, R. Nikam, S. Wanewe, and D. K. B. Naik, “Visual Assistant for Blind People using Raspberry Pi,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 3307, pp. 671–675, 2021, doi: 10.32628/cseit2173142.

F. Salih and S. A. Mysoon Omer, “Raspberry pi as a Video Server,” 2018 Int. Conf. Comput. Control. Electr. Electron. Eng. ICCCEEE 2018, pp. 1–4, 2018, doi: 10.1109/ICCCEEE.2018.8515817.

M. Al-Hammadi, G. Muhammad, W. Abdul, M. Alsulaiman, M. A. Bencherif, and M. A. Mekhtiche, “Hand Gesture Recognition for Sign Language Using 3DCNN,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.2990434.

C. Nuzzi, S. Pasinetti, R. Pagani, G. Coffetti, and G. Sansoni, “HANDS: an RGB-D dataset of static hand-gestures for human-robot interaction,” Data Br., vol. 35, 2021, doi: 10.1016/j.dib.2021.106791.

H. Liang, J. Chang, I. K. Kazmi, J. J. Zhang, and P. Jiao, “Hand gesture-based interactive puppetry system to assist storytelling for children,” Vis. Comput., vol. 33, no. 4, 2017, doi: 10.1007/s00371-016-1272-6.

M. Oudah, A. Al-Naji, and J. Chahl, “Elderly care based on hand gestures using kinect sensor,” Computers, vol. 10, no. 1, 2021, doi: 10.3390/computers10010005.

M. Z. Alksasbeh et al., “Smart hand gestures recognition using K-NN based algorithm for video annotation purposes,” Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 1, 2021, doi: 10.11591/ijeecs.v21.i1.pp242-252.

Y. C. Chu, Y. J. Jhang, T. M. Tai, and W. J. Hwang, “Recognition of hand gesture sequences by accelerometers and gyroscopes,” Appl. Sci., vol. 10, no. 18, 2020, doi: 10.3390/APP10186507.

A. Thakral and A. Vohra, “Comparison between local binary pattern histograms and principal component analysis algorithm in face recognition system,” Proc. 2017 Int. Conf. Smart Technol. Smart Nation, SmartTechCon 2017, pp. 973–978, 2018, doi: 10.1109/SmartTechCon.2017.8358516.

How to Cite
H. Susilawati, A. Rukmana, and F. Nuraeni, “HAND GESTURE AND DETEKSI WAJAHDETECTION USING RASPBERRY PI”, J. Tek. Inform. (JUTIF), vol. 4, no. 1, pp. 171-178, Feb. 2023.