Facial Expression Recognition in Food Studies
Abstract
The integration of technology in various sectors, including food studies, continues to increase exponentially. Computer vision, a research topic which initially started with object detection, facial detection, followed by facial recognition (FR), is currently focused on the more complex facial expression recognition (FER). The present research explores FER and its relevance in food studies. In addition to examining some of the current FER methods, such as Convolutional Neutral Network and Haar Cascade, the research is also developing a pre-processing method to increase the accuracy of FER results. FER methods are generally designed to study customer feedback and reactions towards meals served in the F&B industry. Typically, cameras are set up at different corners of a restaurant to capture facial expressions for analyses. Although one can never truly identify a person's feelings just by expression recognition alone, together with survey and feedback forms, researchers are able to get more reliable and accurate data with regard to a customer's preferences and taste.
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BibTeX
@article{fesabas2026facial,
title = {{Facial Expression Recognition in Food Studies}},
author = {Anantha Krishnan Raman Fesabas and David Asirvatham and Jean-Pierre Poulain},
journal = {Asia-Pacific Journal of Innovation in Hospitality and Tourism},
year = {2026},
month = sep,
url = {https://hal.science/hal-05695574v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Facial Expression Recognition in Food Studies
AU - Anantha Krishnan Raman Fesabas
AU - David Asirvatham
AU - Jean-Pierre Poulain
PY - 2026
DA - 2026/09/16
JO - Asia-Pacific Journal of Innovation in Hospitality and Tourism
UR - https://hal.science/hal-05695574v1
AB - The integration of technology in various sectors, including food studies, continues to increase exponentially. Computer vision, a research topic which initially started with object detection, facial detection, followed by facial recognition (FR), is currently focused on the more complex facial expression recognition (FER). The present research explores FER and its relevance in food studies. In addition to examining some of the current FER methods, such as Convolutional Neutral Network and Haar Cascade, the research is also developing a pre-processing method to increase the accuracy of FER results. FER methods are generally designed to study customer feedback and reactions towards meals served in the F&B industry. Typically, cameras are set up at different corners of a restaurant to capture facial expressions for analyses. Although one can never truly identify a person's feelings just by expression recognition alone, together with survey and feedback forms, researchers are able to get more reliable and accurate data with regard to a customer's preferences and taste.
SN - 2710-6519
ER -