Real-Time Customer Satisfaction Analysis using Facial Expressions and Head Pose Estimation

Abstract

Background/Purpose: Quantification of consumer interest is an interesting, innovative, and promising trend in marketing research. For example, an approach for a salesperson is to observe consumer behaviour during the shopping phase and then recall his interest.  However, the salesperson needs unique skills because every person may interpret their behaviour in a different manner.  The purpose of this research is to track client interest based on head pose positioning and facial expression recognition.

Objective: We are going to develop a quantifiable system for measuring customer interest.  This system recognizes the important facial expression and then processes current client photos and does not save them for later processing. 

Design/Methodology/Approach: The work describes a deep learning-based system for observing customer actions, focusing on interest identification. The suggested approach determines client attention by estimating head posture. The system monitors facial expressions and reports customer interest. The Viola and Jones algorithms are utilized to trim the facial image.

Findings/Results: The proposed method identifies frontal face postures, then segments facial mechanisms that are critical for facial expression identification and creating an iconized face image. Finally, the obtained values of the resulting image are merged with the original one to analyze facial emotions.

Conclusion:  This method combines local part-based features with holistic facial information. The obtained results demonstrate the potential to use the proposed architecture as it is efficient and works in real-time.

Paper Type: Conceptual Research.