Pengenalan Ekspresi Wajah Menggunakan Teknik Filter Wavelet Gabor
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Abstract
Pengenalan Emosi Wajah telah lama menjadi topik hangat dalam penelitian identifikasi pola komputer, dan saat ini tidak ada metode pengenalan ekspresi wajah yang memiliki tingkat pengenalan 100%. Jadi, meningkatkan tingkat pengenalan memerlukan pra-pemrosesan set data yang lebih baik, meningkatkan pendekatan ekstraksi fitur, dan memanfaatkan pengklasifikasi terbaik untuk pengenalan wajah. Penelitian ini bertujuan untuk meningkatkan pendekatan ekstraksi ciri untuk meningkatkan tingkat pengenalan sistem pengenalan ekspresi wajah. Fase penting dalam pengenalan gerakan wajah adalah ekstraksi fitur, yang menentukan tingkat pengenalan. Fitur harus diekstraksi dengan cara atau proyeksi yang berbeda untuk meningkatkan tingkat pengenalan, tetapi ada risiko mengembangkan redundansi, yang akan menurunkan tingkat pengenalan. Sementara Gabor memiliki varian fitur yang maksimal, ia memiliki kesulitan dengan dimensi dan redundansi yang tinggi. Beberapa strategi harus digunakan untuk mengurangi ukuran dan redundansi. Filtering adalah teknik pengurangan dimensi yang digunakan oleh Gabor. Oleh karena itu seluruh proses dikenal sebagai filter Gabor. Pengambilan sampel, pemfilteran rata-rata, dan teknik pemfilteran lainnya adalah contohnya. Fitur-fitur Gabor difilter menggunakan transformasi wavelet dan mendapatkan komponen optimal dari dataset wajah dalam teknik ekstraksi fitur Gabor yang diusulkan, memberikan tingkat pengenalan yang lebih tinggi daripada teknik ekstraksi fitur Gabor rata-rata dan teknik ekstraksi fitur penyaringan Gabor sampling pengenalan ekspresi wajah.
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Accepted 2020-12-04
Published 2021-01-30
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