Assessment of Facial Expressions in Product Appreciation

Title:

Assessment of Facial Expressions in Product Appreciation

Creator:

Popa, M. C., Rothkrantz, L. J. M., Wiggers, P., and Shan, C.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:5b528103-3bf5-4d55-99a2-a867e6c035ea
uuid:5b528103-3bf5-4d55-99a2-a867e6c035ea
doi:10.14311/NNW.2017.27.009

Subject:

product emotions, facial expression analysis, geometric features, appearance features, unsupervised learning, and supervised learning

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In the marketing area, new trends are emerging, as customers are not only interested in the quality of the products or delivered services, but also in a stimulating shopping experience. Creating and influencing customers' experiences has become a valuable differentiation strategy for retailers. Therefore, understanding and assessing the customers' emotional response in relation to products/services represents an important asset. The purpose of this paper consists of investigating whether the customer's facial expressions shown during product appreciation are positive or negative and also which types of emotions are related to product appreciation. We collected a database of emotional facial expressions, by presenting a set of forty product related pictures to a number of test subjects. Next, we analysed the obtained facial expressions, by extracting both geometric and appearance features. Furthermore, we modeled them both in an unsupervised and supervised manner. Clustering techniques proved to be efficient at differentiating between positive and negative facial expressions in 78\% of the cases. Next, we performed more refined analysis of the different types of emotions, by employing different classification methods and we achieved 84\% accuracy for seven emotional classes and 95\% for the positive vs. negative.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:2

Harvested from:

CDK

Metadata only:

false