A Dataset for Comparing Mirrored and Non-Mirrored Male Bust Images for Facial Recognition

Collin Gros and Jeremy Straub
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Collin Gros: Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA
Jeremy Straub: Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA

Data, 2019, vol. 4, issue 1, 1-10

Abstract: Facial recognition, as well as other types of human recognition, have found uses in identification, security, and learning about behavior, among other uses. Because of the high cost of data collection for training purposes, logistical challenges and other impediments, mirroring images has frequently been used to increase the size of data sets. However, while these larger data sets have shown to be beneficial, their comparative level of benefit to the data collection of similar data has not been assessed. This paper presented a data set collected and prepared for this and related research purposes. The data set included both non-occluded and occluded data for mirroring assessment.

Keywords: human male face images; bust images; facial recognition; mirrored images; image comparison; human identification; multiple perspectives (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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