Implementasi Principal Component Analysis (PCA) pada Pengenalan Wajah Resolusi Rendah

  • Reza Phina Tanjung Universitas Dian Nuswantoro
  • Danang Wahyu Utomo Universitas Dian Nuswantoro Semarang
Abstract views: 29 , PDF downloads: 20
Keywords: face recognition, principal component analysis, bounding box, image processing

Abstract

Face recognition involves matching facial features by restricting the facial area. The problem found in the experiment was that the program recognized images outside the face area, especially for low-resolution images. The PCA algorithm and the proposed bounding box approach can identify the facial area and match it with training data. The experiment uses the Yaleface and Face94 datasets in various scenarios, including normal resolution and resolution reduction (75%, 50%, and 25% of the original size). On gif images, the proposed algorithm can produce similarities between the detected image and the input image in a resolution reduction of up to 50%. On jpg images, reducing resolution to 75% does not affect the performance of PCA. The proposed method can recognize faces with similarities in variations of pose and facial expression. The Euclidean value of the jpg image produces a better similarity value than the gif image. 

 

References

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.

T.-V. Dang, “Smart Attendance System based on improved Facial Recognition,” Journal of Robotics and Control (JRC), vol. 4, no. 1, pp. 46–53, Feb. 2023, doi: 10.18196/jrc.v4i1.16808.

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.

I. N. T. A. Putra and E. D. Krisna, “Implementasi Sistem Surveillance Berbasis Pengenalan Wajah pada STMIK STIKOM Indonesia,” Jurnal Ilmu Komputer, vol. 13, no. 2, p. 8, Sep. 2020, doi: 10.24843/JIK.2020.v13.i02.p01.

F. Adiputra and F. Umam, “Presensi Wireless Otomatis menggunakan Face Recognition,” Rekayasa, vol. 15, no. 3, pp. 386–397, Dec. 2022, doi: 10.21107/rekayasa.v15i3.19762.

Siti Khotimatul Wildah, S. Agustiani, Ali Mustopa, Nanik Wuryani, Hendri Mahmud Nawawi, and Rizky Ade Safitri, “Pengenalan Wajah Menggunakan Pembelajaran Mesin Berdasarkan Ekstraksi Fitur Pada Gambar Wajah Berkualitas Rendah,” INFOTECH : Jurnal Informatika & Teknologi, vol. 2, no. 2, pp. 95–103, Dec. 2021, doi: 10.37373/infotech.v2i2.189.

M. Arora and M. Kumar, “AutoFER: PCA and PSO based automatic facial emotion recognition,” Multimed Tools Appl, vol. 80, no. 2, pp. 3039–3049, Jan. 2021, doi: 10.1007/s11042-020-09726-4.

E. Odhiambo Omuya, G. Onyango Okeyo, and M. Waema Kimwele, “Feature Selection for Classification using Principal Component Analysis and Information Gain,” Expert Syst Appl, vol. 174, p. 114765, Jul. 2021, doi: 10.1016/j.eswa.2021.114765.

A. A. Khan, A. A. Shaikh, Z. A. Shaikh, A. A. Laghari, and S. Karim, “IPM-Model: AI and metaheuristic-enabled face recognition using image partial matching for multimedia forensics investigation with genetic algorithm,” Multimed Tools Appl, vol. 81, no. 17, pp. 23533–23549, Jul. 2022, doi: 10.1007/s11042-022-12398-x.

R. Hammouche, A. Attia, S. Akhrouf, and Z. Akhtar, “Gabor filter bank with deep autoencoder based face recognition system,” Expert Syst Appl, vol. 197, p. 116743, Jul. 2022, doi: 10.1016/j.eswa.2022.116743.

N. E. Chalabi, A. Attia, A. Bouziane, and Z. Akhtar, “Particle swarm optimization based block feature selection in face recognition system,” Multimed Tools Appl, vol. 80, no. 24, pp. 33257–33273, Oct. 2021, doi: 10.1007/s11042-021-11367-0.

Y. Li, R. Lu, R. Huang, and W. Zhang, “Research on Face Recognition Algorithm Based on HOG Feature,” J Phys Conf Ser, vol. 1757, no. 1, p. 012099, Jan. 2021, doi: 10.1088/1742-6596/1757/1/012099.

S. Sriyati, A. Setyanto, and E. E. Luthfi, “LITERATURE REVIEW: PENGENALAN WAJAH MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK,” Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN), vol. 8, no. 2, Oct. 2020, doi: 10.30646/tikomsin.v8i2.463.

J. Ma and Y. Yuan, “Dimension reduction of image deep feature using PCA,” J Vis Commun Image Represent, vol. 63, p. 102578, Aug. 2019, doi: 10.1016/j.jvcir.2019.102578.

J. Haris Mita, C. Ganesh Babu, and M. Gowri Shankar, “Performance Analysis of Dimensionality Reduction using PCA, KPCA and LLE for ECG Signals,” IOP Conf Ser Mater Sci Eng, vol. 1084, no. 1, p. 012005, Mar. 2021, doi: 10.1088/1757-899X/1084/1/012005.

R. Kosasih, “Pengenalan Wajah Menggunakan PCA dengan Memperhatikan Jumlah Data Latih dan Vektor Eigen,” Jurnal Informatika Universitas Pamulang, vol. 6, no. 1, p. 1, Mar. 2021, doi: 10.32493/informatika.v6i1.7261.

Md. S. Ejaz, Md. R. Islam, M. Sifatullah, and A. Sarker, “Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition,” in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), IEEE, May 2019, pp. 1–5. doi: 10.1109/ICASERT.2019.8934543.

J. S. Nayak and M. Indiramma, “An approach to enhance age invariant face recognition performance based on gender classification,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 5183–5191, Sep. 2022, doi: 10.1016/j.jksuci.2021.01.005.

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.

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.

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.

Md. A. Marjan, Md. R. Islam, Md. P. Uddin, M. I. Afjal, and Md. Al Mamun, “PCA-based dimensionality reduction for face recognition,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 19, no. 5, p. 1622, Oct. 2021, doi: 10.12928/telkomnika.v19i5.19566.

C. Irawan, E. H. Rachmawanto, C. Atika Sari, and R. Umah Nur, “Klasifikasi Citra Mengkudu Berdasarkan Perhitungan Jarak Piksel pada Algoritma K-Nearest Neighbour,” Infotekmesin, vol. 14, no. 2, pp. 200–207, Jul. 2023, doi: 10.35970/infotekmesin.v14i2.1827.

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Published
2024-01-24