Deteksi Wajah dan Mata dengan Menggunakan Metode Fitur Haar-Like pada Kamera WebCam

Hendro Nugroho, Muchamad Kurniawan, Naili Saidatin

Abstract


Detekesi objek pada komputer vision merupakan hal yang penting, terutama tentang deteksi wajah. Didalam penelitian ini, dilakukan penelitian deteksi wajah dan mata yang digunakan objek orang itu melihat kamera webcam atau tidak. Untuk menunjang penelitian ini, pendekatan metode yang digunakan adalah Haar-Like Fitur. Langkah-langkah penelitian ini adalah input video dari kamera webcam, proses grayscale, penambahan area deteksi, metode Haar-Like fitur, hasil deteksi objek wajah dan mata. Hasil dari deteksi wajah dan mata didapat berupa hasil pada objek terdeteksi wajah dan mata pada saat objek melihat kamera Webcam. Hasil yang tidak berhasil deteksi wajah dan mata disebabkan oleh objek memakai kacamata dan tidak melihat kamera webcam

Keywords


Deteksi wajah dan mata; Grayscale; Haar-Like Fitur; Webcam

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References


X. Sun, P. Wu, and S. C. H. Hoi, “Neurocomputing Face detection using deep learning : An improved faster RCNN approach,” Neurocomputing, vol. 299, pp. 42–50, 2018.

X. Zhao, X. Chai, Z. Niu, C. Heng, and S. Shan, “Context modeling for facial landmark detection based on Non-Adjacent Rectangle ( NAR ) Haar-like feature ?,” IMAVIS, vol. 30, no. 3, pp. 136–146, 2012.

S. Pavani, D. Delgado, and A. F. Frangi, “Haar-like features with optimally weighted rectangles for rapid object detection,” Pattern Recognit., vol. 43, no. 1, pp. 160–172, 2010.

A. Mohamed, A. Issam, B. Mohamed, and B. Abdellatif, “Real-time detection of vehicles using the haar-like features and artificial neuron networks,” Procedia - Procedia Comput. Sci., vol. 73, no. Awict, pp. 24–31, 2015.

K. Park and S. Hwang, “An improved Haar-like feature for efficient object detection q,” PATTERN Recognit. Lett., vol. 42, pp. 148–153, 2014.

D. Lima et al., “Computers in Industry Augmented visualization using homomorphic fi ltering and Haar-based natural markers for power systems substations,” Comput. Ind., vol. 97, pp. 67–75, 2018.

M. Abed, M. Khanapi, A. Ghani, and N. Arunkumar, “A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear,” Futur. Gener. Comput. Syst., vol. 89, pp. 539–547, 2018.

B. Mohamed, A. Issam, A. Mohamed, and B. Abdellatif, “ECG image classification in real time based on the haar-like features and artificial neural networks,” Procedia - Procedia Comput. Sci., vol. 73, no. Awict, pp. 32–39, 2015.


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