HISTOGRAM OF ORIENTED GRADIENT UNTUK DETEKSI EKSPRESI WAJAH MANUSIA
DOI:
https://doi.org/10.52972/hoaq.vol10no2.p81-86Keywords:
expression detection, Histogram of Oriented Gradient, Convolutional Neural NetworkAbstract
This research focuses on the detection of human facial expressions using the Histogram of Oriented Gradient algorithm. Whereas for the classification algorithm, Convolutional Neural Network is used. Image data used in the form of seven different expressions of humans with the extraction of 48x48 pixels. The use of Histogram of Oriented Gradient as a feature extracting algorithm, because Histogram of Oriented Gradient is good to be used in detecting moving objects. Whereas Convolutional Neural Network is used because it is an improvement of the Multi Layer Perceptron algorithm. Of the three epoches done, it produced the best accuracy of 77% re-introduction of human facial expressions. These results are quite convincing because it only uses three epochs.
References
A. Mehrabian “Communication without words”, Psychology Today, vol.2, pp.52-55,1968
T.Kanade, J.F Cohn and Y.Tian “Comprehensive database for facial expression analysis”, in Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG’00), Grenoble, France, 2000
M. Haghighat, S. Zonouz and M. Abdel-Mottaleb, “CloudID : Trustworthy cloud-based and cross-enterprise biometric identification,” Expert Systems with Applications, vol.42 no.21,pp.7905-7916,2015
..
M.Alwakeel and Z. Shaaban, “Face recognition based on Haar wavelet transform and principal component analysis via Levenberg-Marquardt backpropagation neural network,” European Journal of Scientific Research, vol.42, no.1, pp.25-31,2010
M. Goyani and N. Patel, “Multi-Level Haar Wavelet based Facial Expression Recognition using Logistic Regression,” Indian Journal of Science and Technology, vol.10, no.9,2017
.
J. Zhang et al., “Therapeutic detoxification of quercetin against carbon tetrachloride-induced acute liver injury in mice and its mechanism,” J. Zheijang Univ. Sci. B, vol. 15, no.12, pp.1039-1047, 2014
K, Seemanthini and S.S. Manjunath, “Human Detection and Tracking using HOG for Action Recognition,”Procedia COmpt. Sci, vol. 132, no. Iccids, pp.1317-1326,2018
R. Hu, M. Barnard, and J.P. Collomosse, “Gradient field descriptor for sketch based retrieval and localization,” in ICIP, 20 I 0, pp.1025-1028.
N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” dalam one-Alps, 655 avenue de ‘’Europe, Montbonnot 38334, France, 2005
A. Karpathy, “CS231 in Convolutional Neural Network for Visual Recognition, “Stanford University, [Online]. Available: http://cs231n.github.io/neural-networks-1/
I. Corporation, “OpenCV,” Itseez, 2019. [Online]. Available : https://opencv.org/
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Jurnal HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi diterbitkan berdasarkan lisensi Creative Commons Attribution 4.0 International License (CC BY 4.0). Lisensi ini memungkinkan setiap orang untuk Berbagi: menyalin dan mendistribusikan kembali materi ini dalam format atau bentuk apapun; Adaptasi: merombak, mengubah, dan membuat turunan dari materi ini untuk keperluan apa pun, termasuk keperluan komersial, asalkan mereka memberikan pengakuan kepada Penulis Asli atas hasil karya aslinya.