Algoritma Principal Component Analysis (PCA) dan Metode Bounding Box pada Pengenalan Citra Wajah

Habibu Riski; Danang Wahyu Utomo

doi:10.30591/jpit.v9i1.6165

Algoritma Principal Component Analysis (PCA) dan Metode Bounding Box pada Pengenalan Citra Wajah

Habibu Riski, Danang Wahyu Utomo

Abstract

Current facial recognition system utilizes the Principal Component Algorithm (PCA) and the Bounding Box method to recognize facial locations based on brightness levels. The problem that was found in the experiment was that unclear or illuminated light factors could cause inaccuracies in facial area recognition. PCA is an algorithm capable of performing dimensional reduction to recognize the face area. The recognition process involves image pre-processing, PCA analysis to produce vectors, and application of a Bounding Box to focus on critical areas. This research contributes to the development of reliable and efficient facial recognition systems, potentially applied in security and access management. The experiment used the Grimace dataset using the .jpg format, with tests on normal brightness and -50 decreases in brightness level. At the decrease in -50, the result shows that the smallest distance value is 3540.1, and the greatest distance is 6849.4 with the average value being 5810.110. face recognition results can recognize face images with the original image

Keywords

pengenalan wajah, image processing, principle component analysis, bounding box

Full Text:

References

S. Minaee, A. Abdolrashidi, H. Su, M. Bennamoun, and D. Zhang, “Biometrics recognition using deep learning: a survey,” Artif Intell Rev, vol. 56, no. 8, pp. 8647–8695, Aug. 2023, doi: 10.1007/s10462-022-10237-x.

A. L. Akbar, C. Fatichah, and A. Saikhu, “FACE RECOGNITION USING DEEP NEURAL NETWORKS WITH THE COMBINATION OF DISCRETE WAVELET TRANSFORM, STATIONARY WAVELET TRANSFORM, AND DISCRETE COSINE TRANSFORM METHODS,” JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 18, no. 2, p. 158, Jul. 2020, doi: 10.12962/j24068535.v18i2.a1000.

Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face Recognition Systems: A Survey,” Sensors, vol. 20, no. 2, p. 342, Jan. 2020, doi: 10.3390/s20020342.

I. Adjabi, A. Ouahabi, A. Benzaoui, and A. Taleb-Ahmed, “Past, Present, and Future of Face Recognition: A Review,” Electronics (Basel), vol. 9, no. 8, p. 1188, Jul. 2020, doi: 10.3390/electronics9081188.

N. W. Pratiwi, F. Fauziah, S. Andryana, and A. Gunaryati, “Deteksi Wajah Menggunakan Hidden Markov Model (HMM) Berbasis Matlab,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 3, no. 1, p. 44, Aug. 2018, doi: 10.30998/string.v3i1.2538.

Y. Yu, Q. Wang, and M. Jiang, “Discriminative common feature subspace learning for age‐invariant face recognition,” IET Biom, vol. 9, no. 4, pp. 136–142, Jul. 2020, doi: 10.1049/iet-bmt.2019.0104.

F. Sarasati, “Implementasi Metode Principal Component Analysis untuk Sistem Pengenalan Wajah,” Jurnal Infortech, vol. 3, no. 2, pp. 152–156, Dec. 2021, doi: 10.31294/infortech.v3i2.11786.

G. E. Rani, S. M, M. P. Suresh, M. Abhiram, K. J. Surya, and B. Y. A. N. Kumar, “Face Recognition Using Principal Component Analysis,” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, Apr. 2022, pp. 932–936. doi: 10.1109/ICACITE53722.2022.9823434.

M. F. Hidayattullah, Y. Hapsari, M. Humam, and M. Nishom, “Automatic Face Mask Detector menggunakan Algoritma Viola and Jones,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 7, no. 1, pp. 32–36, Jun. 2022, doi: 10.30591/jpit.v7i1.3563.

Sunario Megawan and Wulan Sri Lestari, “Deteksi Spoofing Wajah Menggunakan Faster R-CNN dengan Arsitektur Resnet50 pada Video,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 9, no. 3, pp. 261–267, Aug. 2020, doi: 10.22146/.v9i3.231.

Lia Farokhah, “Perbandingan Metode Deteksi Wajah Menggunakan OpenCV Haar Cascade, OpenCV Single Shot Multibox Detector (SSD) dan DLib CNN,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 3, pp. 609–614, Jun. 2021, doi: 10.29207/resti.v5i3.3125.

H. Z. Ilmadina, D. Apriliani, and D. S. Wibowo, “Deteksi Pengendara Mengantuk dengan Kombinasi Haar Cascade Classifier dan Support Vector Machine,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 7, no. 1, pp. 1–7, Jan. 2022, doi: 10.30591/jpit.v7i1.3346.

A. JAMHARI, “Perancangan Sistem Pengenalan Wajah Secara Real-Time pada CCTV dengan Metode Eigenface:,” Journal of Informatics, Information System, Software Engineering and Applications (INISTA), vol. 2, no. 2, pp. 20–32, May 2020, doi: 10.20895/inista.v2i2.117.

Sparsh, R. Aggarwal, S. Bhardwaj, and K. Sharma, “Face Recognition System Using Image Enhancement With PCA and LDA,” in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), IEEE, Mar. 2022, pp. 1322–1327. doi: 10.1109/ICCMC53470.2022.9753787.

M. N. Kapse and S. Kumar, “Eye-referenced dynamic bounding box for face recognition using light convolutional neural network,” Intelligent Decision Technologies, vol. 16, no. 2, pp. 369–377, Jun. 2022, doi: 10.3233/IDT-210127.

M. Taskiran, N. Kahraman, and C. E. Erdem, “Face recognition: Past, present and future (a review),” Digit Signal Process, vol. 106, p. 102809, Nov. 2020, doi: 10.1016/j.dsp.2020.102809.

Y. Ren, X. Xu, G. Feng, and X. Zhang, “Non-Interactive and secure outsourcing of PCA-Based face recognition,” Comput Secur, vol. 110, p. 102416, Nov. 2021, doi: 10.1016/j.cose.2021.102416.

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, Sep. 2021, doi: 10.1016/j.neucom.2020.08.083.

D. Luo, G. Wen, D. Li, Y. Hu, and E. Huan, “Deep-learning-based face detection using iterative bounding-box regression,” Multimed Tools Appl, vol. 77, no. 19, pp. 24663–24680, Oct. 2018, doi: 10.1007/s11042-018-5658-5.

S. Luo, X. Li, and X. Zhang, “Bounding‐box deep calibration for high performance face detection,” IET Computer Vision, vol. 16, no. 8, pp. 747–758, Dec. 2022, doi: 10.1049/cvi2.12122.

F. Amalya and S. Harlena, “Klasifikasi Buah Berkhasiat Obat Dengan Algoritme Euclidean Distance Menggunakan Ekstraksi Ciri Bentuk dan Tekstur,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 7, no. 2, pp. 67–73, May 2022, doi: 10.30591/jpit.v7i2.3688.

DOI: https://doi.org/10.30591/jpit.v9i1.6165