Customer satisfaction recognition based on facial expression and machine learning techniques

Abstract

Nowadays, Customer satisfaction is important for businesses and organizations. Manual methods exist, namely surveys and distributing questionnaires to customers. However, marketers and businesses are looking for quick ways to get effective and efficient feedback results for their potential customers. In this paper, we propose a new method for facial emotion detection to recognize customer's satisfaction using machine-learning techniques. We used a facial landmark point; we extract geometric features from customer's emotional faces using distances from landmarks points. Indeed, we used distances between the neutral side and the negative or positive feedback. After that, we classified these distances by using different classifier, namely Support Vector Machine (SVM), KNN, Random Forest, Adaboost, and Decision Tree. To assess our method, we verified our algorithm by using JAFFE datasets. The proposed method reveals 98,66% as accuracy for the most performance SVM classifier.