Facial Expression as an Implicit Customers' Feedback and the Challenges

Kasiran, Zolidah; Yahya, Saadiah

doi:10.1109/cgiv.2007.40

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…Emotional states are more directly related to satisfaction from customers compared with other information forms extracted from image and speech. (Kasiran, 2007). Using the automated emotion recognition system, the consultants or agents are able to assess customer satisfaction based on their emotions.…”

Section: Automated Emotion Recognition Systemmentioning

confidence: 99%

Customer Satisfaction through Technological Integration

Seng

Ang

Shing³

2016

International Journal of Technology and Educational Marketing

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This paper presents a review of automated technology integrations for organizations to assess their customer satisfaction levels. The paper also includes a comparison of the common resources that are used to measure customer satisfaction. The main part of the paper subsequently describes the related concerns and challenges that are faced by the business company to realize customer satisfactions. This paper presents a review of automated technology integrations for organizations to assess their customer satisfaction. These components can be integrated into communication tools to solve the existing problems efficiently, and improve the way of assessing customer satisfaction. The limitations or challenges of current approaches in technology related ways to realize the satisfactions are also discussed. The end of the paper gives recommendations and solutions to show the possible ways in solving the existing problems and improving the way of assessing customer satisfaction by integrating the appropriate technology.

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Section: Automated Emotion Recognition Systemmentioning

confidence: 99%

Customer Satisfaction through Technological Integration

Seng

Ang

Shing³

2016

International Journal of Technology and Educational Marketing

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“…Human choices can be analyzed in various ways such as brain images [3]; an electroencephalogram also known as EEG [4], [5]; eye tracking [6], [7]; heart rate registration [8]; and other approaches have been a recent topic in the existing literature. Customer behavior classification [9 -11] and customer face analysis studies have also been used in several studies [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Real Time Customer Satisfaction Analysis using Facial Expressions and Headpose Estimation

S¹,

Koti²,

K.G³

et al. 2022

IJACSA

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One of the most exciting, innovative, and promising topics in marketing research is the quantification of customer interest. This work focuses on interest detection and provides a deep learning-based system that monitors client behaviour. By assessing head position, the recommended method assesses customer attentiveness. Customers whose heads are directed toward the promotion or the item of curiosity are identified by the system, which analyses facial expressions and records client interest. An exclusive method is recommended to recognize frontal face postures first, then splits facial components that are critical for detecting facial expressions into iconized face pictures. Mainly consumer interest monitoring will be executed. Finally, the raw facial images are combined with the iconized face image's confidence ratings to estimate facial emotions. This technique combines local part-based characteristics through holistic face data for precise facial emotion identification. The new method provides the dimension of required marketing and product findings indicate that the suggested architecture has the potential to be implemented because it is efficient and operates in real time.

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“…Knowing the choices using various tools such as brain images [3], EEG [4], [5], eye tracking ( [6]; [7]), heart rate registration [8], and other approaches have been a recent topic in the existing literature. Customer behavior classification [9]; [10] and customer face analysis studies have also been used in several studies [11]. Estimating a client's graphic focus of attention is one approach to gauging their interest.…”

Section: Introductionmentioning

confidence: 99%

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

Nethravathi

Aithal

2022

IJAEML

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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.

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Facial Expression as an Implicit Customers' Feedback and the Challenges

Cited by 12 publications

References 18 publications

Customer Satisfaction through Technological Integration

Customer Satisfaction through Technological Integration

Real Time Customer Satisfaction Analysis using Facial Expressions and Headpose Estimation

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

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