Face Recognition Using Backpropagation Algorithm (Supervised Learning)

Nelson Nainggolan; Eliasta Ketaren; Harni Seven Adinata

Authors

Nelson Nainggolan Faculty of Mathematics and Natural Sciences, Sam Ratulangi University
Eliasta Ketaren Faculty of Mathematics and Natural Sciences, Sam Ratulangi University
Harni Seven Adinata Faculty of Engineering, Sam Ratulangi University

Keywords:

Backdropagation, Face Recognition, Image Processing, Supervised Learning

Abstract

Image processing plays a pretty important role. Much research has been carried out on image objects, where information from images can contribute to and benefit education, technological innovation, and information management. The face is a marker of recognizing someone. Facial recognition systems have difficulty with different facial orientations, lighting, haircuts, mustaches or beards, glasses, permanent blemishes or scars, and differences in conditions such as the person turning slightly, looking down, or looking up. The research aims to carry out digital image processing, specifically the process of scaling, grayscale, edge detection, and thresholding the image of a person's face as input. This research is crucial because image processing, especially facial recognition, is essential in health, education, economics, and security. The research uses the Artificial Neural Network method. Artificial Neural Networks are information processing systems designed to imitate how the human brain works in solving a problem by learning by changing the synapse weights. Research results in facial recognition using backpropagation show high accuracy and a fast average time. The suitability level of the recognition results depends on the combination of parameter values used in the learning process. The greater the epoch (repetition), the greater the learning rate; the smaller the error, the higher the known level.

Downloads

Download data is not yet available.

References

Aruna, S. et al. (2024) ‘Analysis of Object Recognition Using Back Propagation-Based Algorithms’, in Emerging Advancements in AI and Big Data Technologies in Business and Society. IGI Global, pp. 313–327.

Cahyo, N.R.D. and Al-Ghiffary, M.M.I. (2024) ‘An Image Processing Study: Image Enhancement, Image Segmentation, and Image Classification using Milkfish Freshness Images’, International Journal of Engineering Computing Advanced Research, 1(1), pp. 11–22.

Chandran, P. et al. (2023) ‘Face Recognition Using LBPH, Haar Cascade Classifier and Back Propagation Neural Network’, in International Conference on Recent Advances in Artificial Intelligence & Smart Applications. Springer, pp. 207–219.

Khan, U. et al. (2024) ‘Four decades of image processing: a bibliometric analysis’, Library Hi Tech, 42(1), pp. 180–202.

Madasamy, N.S. et al. (2024) ‘A Novel Back-Propagation Neural Network for Intelligent Cyber-Physical Systems for Wireless Communications’, IETE Journal of Research, 70(2), pp. 1361–1373.

Manalu, P.D. and Situmorang, Z. (2024) ‘Face Recognition Using Support Vector Machine (SVM) and Backpropagation Neural Network (BNN) Methods to Identify Gender on Student Identity Cards’, in Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC).

Niu, Y. and Wang, Z. (2024) ‘Face Recognition with Siamese Networks’, in Journal of Physics: Conference Series. IOP Publishing, p. 12008.

Peng, J. (2024) ‘Application of Backpropagation Neural Network based on Pytorch Framework in Handwritten Digit Recognition’, in 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, pp. 549–553.

Purba, R.A. (2017) ‘Pendeteksian Sisi Tepi Objek Citra Digital Menggunakan Operator Laplace Dengan Teknik Konvolusi Untuk Menghasilkan Bahan Dasar Menggambar Anak’, Semnasteknomedia Online, 5(1), pp. 2–4.

Purba, R.A. et al. (2019) ‘Edge image detection combines Laplace operation with convolution technique to produce drawing materials for children’, Journal of Physics: Conference Series, 1402, p. 066097. doi:10.1088/1742-6596/1402/6/066097.

Purba, R.A. et al. (2020) ‘The optimalization of backpropagation neural networks to simplify decision making’, in IOP Conference Series: Materials Science and Engineering. doi:10.1088/1757-899X/830/2/022091.

Purba, R.A. (2021) ‘Application design to help predict market demand using the waterfall method’, Matrix: Jurnal Manajemen Teknologi dan Informatika, 11(3), pp. 140–149.

Purba, R.A. (2022) ‘Hybrid Models with Technology: Is it Effective for Learning in Abnormal Situations?’, Journal of Education Research and Evaluation, 6(1).

Purba, R.A. and Sembiring, J. (2016) ‘Selection of scholarship recipients by using Promethee method in Polytechnic Unggul LP3M Medan’, 2016 International Seminar on Application for Technology of Information and Communication (ISemantic), pp. 86–92. doi:10.1109/ISEMANTIC.2016.7873815.

Purba, R.A. and Sondang, S. (2022) ‘Design and Build Monitoring System for Pregnant Mothers and Newborns using the Waterfall Model’, INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, 6(1), pp. 29–42.

Purba, R.A. and Verawardina, U. (2021) ‘Deteksi Mahasiswa Yang Dapat Menyusun Tugas Akhir dengan Metode Visekriterijumsko Kompromisno Rangiranje (VIKOR)’, Techno. Com, 20(2), pp. 210–220.

Srivastava, S. and Sharma, H. (2024) ‘Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks’, Multimedia Tools and Applications, 83(5), pp. 14085–14109.

Sudhagar, S., Sakthivel, M. and Daniel, S.A.A. (2020) ‘Application of image processing to radiographic image for quantitative assessment of friction stir welding quality of aluminium 2024 alloy’, Measurement, 152, p. 107294.

Zhai, M. (2024) ‘Human Action Recognition Based on Back Propagation Neural Network’, in 2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, pp. 1–6.