A DIGITAL PRINTING APPLICATION AS AN EXPRESSION IDENTIFICATION SYSTEM.

Arman Arman; Prasetya Prasetya; Feny Nurvita Arifany; Fertilia Budi Pradnyaparamita; Joni Laksito

doi:10.51903/jtie.v1i1.135

Arman Arman Universitas Sains dan Teknologi Komputer
Prasetya Prasetya Universitas Sains dan Teknologi Komputer
Feny Nurvita Arifany Universitas Sains dan Teknologi Komputer
Fertilia Budi Pradnyaparamita Universitas Sains dan Teknologi Komputer
Joni Laksito Universitas Sains dan Teknologi Komputer

DOI: https://doi.org/10.51903/jtie.v1i1.135

Keywords: Human Computer Interaction, facial emotion recognition, facial expressions, facial action coding system, classifier combination, facial features, AU-Coded facial expression, CK database, digital printing.

Abstract

Human Computer Interaction (HCI), a growing research field in science and engineering, aims to provide a natural way for humans to use computers as tools. Humans prefer to interact with each other mainly through speech, but also through facial expressions and gestures, for certain parts of the speech and displays of emotions. The identity, age, gender, and emotional state of a person can be obtained from his face. The impression we receive from the expression reflected on the face affects our interpretation of the spoken word and even our attitude towards the speaker himself. Although emotion recognition is an easy task for humans, it still proves to be a difficult task for computers to recognize user`s emotional state. Advances in this area promise to arm our technological environment by means for more effective interactions with humans, and hopefully the impact of facial expressions on cognition will increase rapidly in the future. Will do. In recent years, the adoption of digital has increased rapidly, and the quality has improved significantly. Digital printing has resulted in fast delivery and needs-based costs. This article describes a sophisticated combination classifier approach, an empirical study of ensembles, stacking, and voting. These three approaches were tested on Nave Bayes (NB), Kernel Naive Bayes (kNB), Neural Network (NN), Auto MultiLayer Perceptron (Auto MLP), and Decision Tree (DT), respectively. The main contribution of this paper is the improvement of the classification accuracy of facial expression recognition tasks. In both persondependent and nonpersondependent experiments we showed that using a combination of these classifier combinations gave significantly better results than using individual classifiers. It has been observed from experiments that the overall voting technique by voting achieves the best classification accuracy.

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