Penerapan Histogram of Oriented Gradients, Principal Component Analysis dan AdaBoost untuk Sistem Pengenalan Wajah

Adhika Gunadarma, Ken Ratri Retno Wardani

Abstract

This The human face image has a lot of information that can be used in the field of computer vision to create a human face recognition system. The method used in this study is the Histogram of Oriented Gradients (HOG) method used for feature extraction. The Principal Component Analysis (PCA) method is applied from the features of the HOG method to reduce the dimensionality of feature data from high to low without losing much of the information. Finally, the Adaptive Boosting method (AdaBoost) is used to process the resulting feature classification. Before performing facial recognition process, the initial treatment is done to detect and cut the face of the next part of the image pieces will be the same size so that the face taken has a uniform size. Based on the test results of cell, block and bins values, the best total eigenvalue and total iteration for this process were 8,16,4, -, 15 for the classifier using the HOG plus AdaBoost method with the resulting accuracy to recognize the face of 86% and 8.16,16,20,10 for classifier using HOG method, PCA with AdaBoost with accuracy level for face recognition of 96%.

Citra wajah manusia memiliki banyak informasi yang dapat digunakan pada bidang komputer vision untuk membuat sistem pengenalan wajah manusia. Metode yang digunakan pada penelitian kali ini adalah metode Histogram of Oriented Gradients (HOG) yang digunakan untuk ekstraksi fitur. Metode Principal Component Analysis (PCA) diterapkan dari hasil fitur metode HOG untuk mereduksi dimensionalitas data fitur dari tinggi ke rendah tanpa menghilangkan banyak informasi. Terakhir, metode Adaptive Boosting (AdaBoost) dipakai untuk proses klasifikasi fitur yang dihasilkan. Sebelum melakukan proses pengenalan wajah, dilakukan pengolahan awal untuk mendeteksi dan memotong bagian wajah yang selanjutnya bagian potongan citra akan di samakan ukurannya agar wajah yang terambil mempunyai ukuran seragam. Berdasarkan hasil pengujian nilai sel, block dan bins, jumlah eigen dan jumlah iterasi terbaik untuk keseluruhan pada proses ini adalah 8,16,4,-,15 untuk classifier menggunakan metode HOG dan AdaBoost  dengan tingkat akurasi yang dihasilkan untuk mengenali wajah sebesar 86% dan 8,16,16,20,10 untuk classifier menggunakan metode HOG, PCA dengan AdaBoost dengan tingkat akurasi untuk pengenalan wajah sebesar 96%.

Keywords

sistem pengenalan wajah, deteksi wajah, HAAR, histogram of oriented gradients, principal component analysis, adaptive boosting

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References

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