|Title||Electro-optical seasonal weather and gender data collection|
|Publication Type||Conference Proceedings|
|Year of Publication||2013|
|Authors||Ryan McCoppin, Nathan Koester, Nathan Rude, Mateen Rizki, Louis Tamburino, Andrew Freeman, Olga Mendoza-Schrock|
|Conference Name||SPIE 8751, Machine Intelligence and Bio-inspired Computation: Theory and Applications VII|
This paper describes the process used to collect the Seasonal Weather And Gender (SWAG) dataset; an electro-optical dataset of human subjects that can be used to develop advanced gender classification algorithms. Several novel features characterize this ongoing effort (1) the human subjects self-label their gender by performing a specific action during the data collection and (2) the data collection will span months and even years resulting in a dataset containing realistic levels and types of clothing corresponding to the various seasons and weather conditions. It is envisioned that this type of data will support the development and evaluation of more robust gender classification systems that are capable of accurate gender recognition under extended operating conditions.