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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Collin Gros & Jeremy Straub, 2019. "A Dataset for Comparing Mirrored and Non-Mirrored Male Bust Images for Facial Recognition," Data, MDPI, vol. 4(1), pages 1-10, February.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:1:p:26-:d:204382
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    Citations

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    Cited by:

    1. Collin Gros & Jeremy Straub, 2019. "Video Recordings of Male Face and Neck Movements for Facial Recognition and Other Purposes," Data, MDPI, vol. 4(3), pages 1-6, September.

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