Face Recognition Dengan Metode Haar Cascade dan Facenet
Abstract
Pengenalan wajah adalah suatu metode pengenalan yang berorientasi pada wajah. Pengenalan citra wajah manusia merupakan salah satu teknologi yang berkembang pada bidang computer vision dengan penerapannya dalam sistem pengenalan biometrik, pencarian, pengindeksan pada database video digital, keamanan kontrol akses area terbatas, konferensi video, dan interaksi manusia dengan komputer. Algoritma Haar Cascade Classifier adalah salah satu algoritma yang digunakan untuk mendeteksi sebuah wajah. Algoritma Haar Cascade Classifier memiliki kelebihan yaitu perihal komputasi yang cepat karena tersebut hanya bergantung pada jumlah piksel dalam persegi dari sebuah image. Pengenalanan wajah yang diusulkan menggunakan objek wajah yang bervariasi posisinya dari hasil capture pada sebuah webcam yang terkoneksi pada sebuah komputer atau menggunakan webcam bawaan laptop.
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References
Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, “Traffic-Sign Detection and Classification in the Wild,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 2110–2118, 2016, doi: 10.1109/CVPR.2016.232.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.
T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, “Focal Loss for Dense Object Detection,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2017-Octob, pp. 2999–3007, 2017, doi: 10.1109/ICCV.2017.324.
R. Girshick, “Fast R-CNN,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2015 Inter, pp. 1440–1448, 2015, doi: 10.1109/ICCV.2015.169.
C. C. B, M. Liu, O. Tuzel, and J. Xiao, “R-CNN for Small Object Detection,” vol. 1, pp. 214–230, 2017, doi: 10.1007/978-3-319-54193-8.
A. El-Sawy, M. Loey, and H. El-Bakry, “Arabic Handwritten Characters Recognition using Convolutional Neural Network,” 2019 10th Int. Conf. Inf. Commun. Syst. ICICS 2019, vol. 5, pp. 147–151, 2017, doi: 10.1109/IACS.2019.8809122.
H. Rampersad, “Developing,” Total Perform. Scorec., pp. 159–183, 2020, doi: 10.4324/9780080519340-12.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 580–587, 2014, doi: 10.1109/CVPR.2014.81.
S. Sahar, “Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penyakit Jantung,” Indones. J. Data Sci., vol. 1, no. 3, pp. 79–86, 2020, doi: 10.33096/ijodas.v1i3.20.
M. M. Baharuddin, T. Hasanuddin, and H. Azis, “Analisis Performa Metode K-Nearest Neighbor untuk Identifikasi Jenis Kaca,” Ilk. J. Ilm., vol. 11, no. 28, pp. 269–274, 2019.
H. Azis, F. T. Admojo, and E. Susanti, “Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah,” Techno.Com, vol. 19, no. 3, 2020.
D. Cahyanti, A. Rahmayani, and S. Ainy, “Analisis performa metode Knn pada Dataset pasien pengidap Kanker Payudara,” Indones. J. Data Sci., vol. 1, no. 2, pp. 39–43, 2020.
A. A. D. Halim and S. Anraeni, “Analisis Klasifikasi Dataset Citra Penyakit Pneumonia menggunakan Metode K-Nearest Neighbor (KNN),” Indones. J. Data Sci., vol. 2, no. 1, pp. 01–12, 2021, doi: 10.33096/ijodas.v2i1.23.
I. P. Putri, “Analisis Performa Metode K- Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular,” Indones. J. Data Sci., vol. 2, no. 1, pp. 21–28, 2021, doi: 10.33096/ijodas.v2i1.25.
L. Saiman and R. Satra, “Analisis performa metode Support Vector Machine untuk klasifikasi dataset aroma tahu berformalin,” Indones. J. Data Sci., vol. 2, no. 2, pp. 50–61, 2021.

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