In this task the goal is to determine the gender of the persons depicted in the individual images.
We propose to use the following evaluation metrics:
- Accuracy (ACC)
- True Positive Rate (TPR)
- True Negative Rate (TNR)
- Area under the Receiver Operator Characteristic Curve (AUC)
The label/fold files are structured such that there is one line per image. Each line starts with the filename followed by the fold ID and the gender. Each of the values is separated by a tab. The gender can be either M for male or F for female. An example line looks like the following:
224896_00M25.JPG 0 M
where 224896_00M25.JPG is the filename of the image, 0 is the fold ID, and M shows that the person depicted on the image is of male gender.
For evaluating gender classification approaches 5-fold cross-validation shall be used for both conditions. To prevent algorithms from learning the identity of the persons in the training set rather than the gender it has to be made sure that all images of individual subjects are only in one fold at a time. Additionally, the folds are selected in such a way that the distribution of age, gender and ethnicity in the folds is similar to the distribution in the whole database. The file lists for these folds can be found in the Downloads section below.
We propose to use the MORPH-II database for the controlled labratory condition for gender classification.
For the uncontrolled condition we decided to use the Labeled Faces in the Wild (LFW) dataset.
- Complete Evaluation Guidelines
- Folds for controlled condition of gender classification (old MORPH distribution)
- Folds for controlled condition of gender classification (new MORPH distribution; thanks to Nesli Erdogmus)
- Folds for controlled condition of gender classification on balanced dataset
- Folds for uncontrolled condition of gender classification
- Folds for uncontrolled condition of gender classification on balanced dataset
- Tobias Gehrig, Karlsruhe Institute of Technology, Germany
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller
|Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments
University of Massachusetts, Amherst, Tech. Rep., Oct. 2007.
K. Ricanek Jr. and T. Tesafaye
|MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
In IEEE 7th International Conference on Automatic Face and Gesture Recognition (FGR’06), Southampton, UK, Apr. 2006, pp. 341–345.
Single- and Cross-Database Benchmarks for Gender Classification Under Unconstrained Settings
- Pablo Dago-Casas, GRADIANT
- Daniel González-Jiménez, GRADIANT
- José Luis Alba-Castro, Universidade de Vigo
- Long Long Yu, GRADIANT
P. Dago-Casas, D. González-Jiménez, L. Long-Yu, and J. L. Alba-Castro
|Single- and Cross- Database Benchmarks for Gender Classification Under Unconstrained Settings
in Proc. First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies, in conjunction with ICCV2011, Barcelona, Spain, 13 Nov. 2011.
A. Gallagher and T. Chen
|Understanding images of groups of people
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 256-263, 2009.