Studi Komparasi Ekstraksi Fitur pada Pengenalan Wajah Menggunakan Principal Component Analysis (PCA) dan Wavelet Daubechies

Riskyana Dewi Intan P; Elly Matul Imah

doi:10.14710/vol%viss%ipp%p

DOI: https://doi.org/10.14710/vol%25viss%25ipp%25p

Studi Komparasi Ekstraksi Fitur pada Pengenalan Wajah Menggunakan Principal Component Analysis (PCA) dan Wavelet Daubechies

Riskyana Dewi Intan P , Elly Matul Imah

University State of Surabaya, Indonesia

Published: 31 Oct 2015.

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Abstract

Paper ini membahas perbandingan ekstraksi fitur untuk pengenalan wajah menggunakan metode Principal Component Analysis (PCA) dan Wavelet Daubechies untuk pengenalan wajah . Basis wavelet daubechies yang digunakan adalah wavelet db2, db4, dan db8. Setiap dekomposisi dilakukan hingga level ke-3 yang kemudian diambil fitur aproksimasi wavelet dan fitur statistik wavelet. Variasi nilai komponen utama dimulai dari nilai komponen ke-1 hingga nilai komponen ke-100 dari 4096 nilai eigen. Nilai komponen ke-1 memiliki presentase sebesar 62% sedangkan nilai komponen ke-100 memiliki presentase sebesar 99% dari total nilai eigen,. Pengujian sistem menggunakan 216 citra wajah yang diambil dari dataset The Japanese Female Facial Expression (JAFFE) yang terdiri dari 10 individu dengan masing-masing sekitar 20 wajah per- individu. Pemilihan data train dan data tes menggunakan cross validation dengan rata-rata akurasi 94.42%. Dari hasil percobaan menggunakan Random Forest Classifier diperoleh tingkat pengenalan tertinggi untuk ekstraksi menggunakan PCA sebesar 100% pada variasi data 95% ,sedangkan tingkat pengenalan tertinggi untuk ekstraksi menggunakan Wavelet Daubechies sebesar 98.611% pada wavelet db2 menggunakan fitur aproksimasi wavelet.

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Keywords: ekstraksi fitur; PCA; pengenalan wajah; Random Forest Classifier;Wavelet Daubechies

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Section: Research Article

Language : ID

In Vol 6, No 12 (2015): JURNAL MASYARAKAT INFORMATIKA

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