Perception of Facial Impressions Using Explicit Features of the Face (xFoFs)

Jihyeon Yeom, Jeongin Lee, Heekyung Yang () and Kyungha Min ()
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Jihyeon Yeom: Department of Computer Science, Sangmyung University, 20, Hongjimun 2-gil, Jongno-gu, Seoul 03016, Republic of Korea
Jeongin Lee: Department of Computer Science, Sangmyung University, 20, Hongjimun 2-gil, Jongno-gu, Seoul 03016, Republic of Korea
Heekyung Yang: Department of Software, Sangmyung University, 31, Sangmyeongdae-gil, Dongnam-gu, Cheonan 31066, Republic of Korea
Kyungha Min: Department of Computer Science, Sangmyung University, 20, Hongjimun 2-gil, Jongno-gu, Seoul 03016, Republic of Korea

Mathematics, 2023, vol. 11, issue 17, 1-28

Abstract: We present a novel approach to perceiving facial impressions by defining the explicit features of the face (xFoFs) based on anthropometric studies. The xFoFs estimate 35 anthropometric features of human faces with normal expressions and frontalized poses. Using these xFoFs, we have developed a method to objectively measure facial impressions, compiling a dataset of approximately 4896 facial images to validate our method. The ranking of xFoFs among the face image dataset guides an objective and quantitative estimation of facial impressions. To further corroborate our study, we conducted two user studies: an examination of the first and strongest impression perception and a validation of the consistency of multiple important impression perceptions. Our work significantly contributes to the field of facial recognition and explainable artificial intelligence (XAI) by providing an effective solution for integrating xFoFs with existing facial recognition models.

Keywords: facial features; face recognition; explainable AI; anthropometry (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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