Klasifikasi Micro-Expression Menggunakan K-Nearest Neighbors Menggunakan Fitur CAS dan HOG
DOI:
https://doi.org/10.52985/insyst.v5i2.346Keywords:
CAS, HOG, K-NN, Micro-ExpressionAbstract
Micro-Expression adalah ekspresi yang muncul dalam waktu singkat, hanya berlangsung sepersekian detik. Hal ini mungkin merupakan akibat dari aktivitas komunikasi antar manusia selama interaksi sosial. Reaksi ekspresi mikro wajah terjadi secara alami dan segera, sehingga hanya menyisakan sedikit ruang untuk manipulasi. Namun, karena Micro-Expression bersifat sementara dan memiliki intensitas rendah, pengenalan dan pengenalannya sulit dan sangat bergantung pada pengalaman para ahli. Karena kekhususan dan kompleksitas intrinsiknya, klasifikasi Micro-Expression menggunakan 2 ekstraksi yaitu CAS dan HOG menarik tetapi menantang, dan baru-baru ini menjadi area penelitian yang aktif. context-aware saliency (CAS) yang bertujuan untuk mendeteksi wilayah gambar yang mewakili pemandangan. Tutujuannya adalah untuk mendeteksi objek dominan. Histogram Oriented Gradient (HOG) Bertujuan sebagai deskriptor yang efektif untuk pengenalan dan deteksi objek. Metode K-Nearest Neighbors (K-NN) digunakan untuk klasifikasi Micro-Expression berdasarkan fitur HOG dari citra saliency. Dataset yang digunakan pada penelitian ini dari data sampel siswa SMK Ma’arif NU Prambon jurusan Multimedia sebanyak 45 siswa dan ditambahkan dataset dari affecnet. Hasil yang didapatkan dari total dataset sebanyak 4116 citra yang dibagi menjadi 6 Micro-Expression yaitu anger, disgust, fear, happy, sad dan surprise, mendapatkan hasil akurasi diatas 80% dari perbandingan dataset sejumlah 4116 terbagi menjadi 2 dengan persentase 70% training dan 30% data testing.
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