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

1
Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA
2
Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA
*
Author to whom correspondence should be addressed.
Received: 13 January 2019 / Revised: 30 January 2019 / Accepted: 5 February 2019 / Published: 8 February 2019
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PDF [3470 KB, uploaded 25 February 2019]
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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. View Full-Text
Keywords: human male face images; bust images; facial recognition; mirrored images; image comparison; human identification; multiple perspectives human male face images; bust images; facial recognition; mirrored images; image comparison; human identification; multiple perspectives
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gros, C.; Straub, J. A Dataset for Comparing Mirrored and Non-Mirrored Male Bust Images for Facial Recognition. Data 2019, 4, 26.

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