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

Nethravathi P. S., P. Aithal
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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.
基于面部表情和头姿估计的实时顾客满意度分析
背景/目的:消费者兴趣的量化是市场研究中一个有趣的、创新的、有前途的趋势。例如,销售人员的一种方法是在购物阶段观察消费者的行为,然后回忆他的兴趣。然而,销售人员需要独特的技能,因为每个人都可能以不同的方式解释他们的行为。本研究的目的是基于头部姿势定位和面部表情识别来跟踪客户的兴趣。目标:我们将开发一个可量化的系统来衡量客户的兴趣。该系统识别重要的面部表情,然后处理当前客户的照片,而不保存它们以供以后处理。设计/方法论/方法:该工作描述了一个基于深度学习的系统,用于观察客户行为,重点是兴趣识别。建议的方法是通过估计头部姿势来确定病人的注意力。该系统监测面部表情并报告顾客的兴趣。使用维奥拉和琼斯算法来修剪面部图像。发现/结果:该方法识别正面面部姿势,然后分割面部机制,这对面部表情识别和创建图标化的面部图像至关重要。最后,将得到的图像值与原始图像合并,进行面部情绪分析。结论:该方法将局部局部特征与整体面部信息相结合。所获得的结果证明了使用所提出的体系结构的潜力,因为它是有效的和实时的。论文类型:概念研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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