RANCANG BANGUN SISTEM PENGENALAN EKSPRESI WAJAH MENGGUNAKAN FISHERFACE DAN JARINGAN SYARAF TIRUAN BACKPROPAGATION

Z Abidin(1),

DOI: https://doi.org/10.15294/ijmns.v35i2.2628


(1) Jurusan Matematika, FMIPA UNNES, Indonesia Gedung D7 lantai 1 Kampus Sekaran Semarang 50229

Abstract

Di dalam kehidupan sehari-hari, khususnya dalam komunikasi interpersonal, wajah sering digunakan untuk berekspresi. Melalui ekspresi wajah, maka dapat dipahami emosi yang sedang bergejolak pada diri individu. Ekspresi wajah merupakan salah satu karakteristik perilaku. Penggunaan sistem teknologi biometrika dengan karakteristik ekspresi wajah memungkinkan untuk mengenali mood atau emosi seseorang. Komponen dasar sistem analisis ekspresi wajah adalah deteksi wajah, ekstraksi data wajah, dan pengenalan ekspresi wajah. Sehingga untuk membangun sebuah sistem pengenal ekspersi wajah, maka perlu dirancang tiga buah sub sistem yaitu sistem deteksi wajah, sistem pembelajaran jaringan syaraf tiruan. Prinsipnya data wajah yang telah dideteksi, diolah menggunakan fisherface, yang selanjutnya hasilnya digunakan sebagai input untuk jaringan syaraf tiruan. Bobot yang dihasilkan pada saat proses pembelajaran jaringan syaraf tiruan inilah yang akan digunakan untuk pengenalan ekspresi wajah.

 

In daily life, especially in interpersonal communication, face often used for express of emotions. Facial expressions are the facial changes in response to a person’s internal emotional states. A facial expression is one of the behavioral characteristics. The use of facial expression characteristics enables to recognize of person’s mood. Basic components of a facial expression analysis system are face detection, face data extraction, and facial expression recognition. So that, to build a facial expression recognition system, it should be designed three subsystems, namely face detection system, learning of neural network system, and facial expression recognition system itself. In principle, face data that has been successfully detected, then it will be constructed by fisherface, and the results of it will be used as an input of neural network. Afterwards, the weights of neural network learning will be used to recognize facial expression.

Keywords

facial expression; fisherface; artificial neural network

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