Penerapan Metode Single-Layer Feed-Forward Neural Network Menggunakan Kernal Gabor untuk Pengenalan Ekspresi Wajah
DOI:
https://doi.org/10.61769/telematika.v12i1.178Keywords:
Klasifikasi, Computer Vision, Expression, Gabor Filter, AdaboostAbstract
Expression is a common thing shown by humans to respond to an event. The face is one of the mediums that humans use to show their expressions. Facial expression consists of 7 are happy, sad, angry, scared, disgusted, shocked, and neutral. Humans can easily recognize expressions issued by a person and use them to be able to determine what reaction should be done to that person. It can be utilized by computers in order to interact more naturally with humans or can be utilized in the medical field to help the treatment of patients. In this study the method used to recognize facial expressions is Gabor as a method to extract features, Adaboost is used to select features, and neural networks are a method to classify facial expressions. For testing use the manually selected JAFFE dataset to remove imagery that has an expression that is less suited to the label. Using this method the introduction of facial expressions managed to get an accuracy of 52%. The results show that the Gabor function has a greater influence on the accuracy of AdaBoost than the parameter changes θ (theta) and σ (sigma).
Ekspresi merupakan hal yang biasa ditunjukkan oleh manusia untuk merespons suatu kejadian. Wajah adalah salah satu media yang digunakan manusia untuk menunjukkan ekspresinya. Ekspresi wajah terdiri atas 7 yaitu senang, sedih, marah, takut, jijik, terkejut, dan netral. Manusia dapat dengan mudah mengenali ekspresi yang dikeluarkan oleh seseorang dan menggunakan untuk dapat menentukan apa reaksi yang harus dilakukan pada orang tersebut sesuai dengan ekspresi yang dikeluarkan. Hal tersebut dapat di manfaatkan oleh komputer untuk dapat berinteraksi lebih natural dengan manusia, selain itu dapat dimanfaatkan dalam bidang medis untuk membantu pengobatan pasien. Dalam penelitian ini metode yang digunakan agar dapat mengenali ekspresi wajah manusia adalah Gabor sebagai metode untuk mengekstraksi fitur, Adaboost digunakan untuk menyeleksi fitur, dan neural network merupakan metode untuk mengklasifikasi ekspresi wajah. Untuk pengujian menggunakan dataset JAFFE yang sudah diseleksi secara manual untuk menghilangkan citra yang memiliki ekspresi yang kurang sesuai dengan label. Menggunakan metode – metode tersebut pengenalan ekspresi wajah berhasil mendapatkan akurasi sebesar 52%. Hasil dari penelitian menunjukan bahwa fungsi Gabor lebih berpengaruh terhadap akurasi AdaBoost dibandingkan dengan mengubah parameter θ (theta) dan σ (sigma).
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