Research / Computer vision · Dataset methodology

Database Augmentation and Independence Testing for Facial Recognition

Database Augmentation and Independence Testing for Facial Recognition. A dataset-and-methodology study examining augmented data across four recognition approaches.

Accepted for IW-FCV 2026 presentation in Tokushima University, Tokushima, Japan

Database Augmentation and Independence Testing for Facial Recognition — original concept placeholder, not gameplay

My role

Primary Author

Team

6 authors

Built with

LBPH · Eigenfaces · Fisherfaces · SFace

Year

2026

01 / MY CONTRIBUTION

The work behind the experience.

I worked on the database and experimental components of a facial-recognition research project, with my main contribution focused on database augmentation. I helped prepare and expand the facial-image dataset through augmentation techniques, increasing the amount and variation of data available for evaluating the recognition pipeline. I also contributed to the independence testing process, including preparing experimental data, running tests, organizing results, and supporting the analysis of recognition performance under different conditions. I collaborated with the research team on integrating these findings into the broader facial-recognition pipeline and preparing the research for presentation at IW-FCV 2026.

02 / OUTCOME

Where it led.

Accepted for presentation at IW-FCV 2026. Scheduled for the October 1 poster session. Proceedings publication is a separate process; no headline benchmark score is claimed.

Andrew Eroyla, Kyle Yuan Uy, John Roland Octavio, and Dr. Weon Geun Oh. Full publication credits will be added with the final paper.

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