01667nas a2200229   4500000000100000000000100001008004100002260001200043653004000055653002200095653002300117653000800140100002500148700001700173700002000190245013300210856008100343300000900424490001300433520097700446022001401423       9998                            d  c04/202310aExplainable Artificial Intelligence10aFacial Expression10aGender Differences10aXAI1 aCristina Manresa-Yee1 aSilvia Ramis1 aJosé M. Buades00aAnalysis of Gender Differences in Facial Expression Recognition Based on Deep Learning Using Explainable Artificial Intelligence  uhttps://www.ijimai.org/journal/sites/default/files/2023-04/ip2023_04_003.pdf  a1-100 vIn Press3 aPotential uses of automated Facial Expression Recognition (FER) cover a wide range of applications such as customer behavior analysis, healthcare applications or providing personalized services. Data for machine learning play a fundamental role, therefore, understanding the relevancy of the data in the outcomes is of utmost importance. In this work we present a study on how gender influences the learning of a FER system. We analyze with Explainable Artificial intelligence (XAI) techniques how gender contributes to the learning and assess which facial expressions are more similar regarding face regions that impact on the classification.
Results show that there exist common regions in some expressions both for females and males with different intensities (e.g. happiness); however, there are other expressions like disgust, where important face regions differ. The insights of this work will help improving FER systems and understand the source of any inequality.  a1989-1660