Benjamin Yip, Garrett Bingham, Katherine Kempfert, Jonathan Fabish, Troy Kling, Cuixian Chen, and Yishi Wang
2018 IEEE International Conference on Big Data
My contribution was discovering thousands of gender, race, and birthdate inconsistencies in the MORPH-II face image dataset that previously published
research had missed. In this paper we discuss our strategy to fix these errors and release these corrections in the hope that future research utilizing MORPH-II
will be more accurate.
Random Subspace Two-dimensional LDA (RS-2DLDA) is a novel machine learning algorithm that improves upon a 2D generalization of LDA in which
the input data is left in matrix form instead of being vectorized. RS-2DLDA builds an ensemble of classifiers by performing k-nearest neighbor
classification in subspaces defined by random selections of the feature vectors learned during training. This gives high accuracy and prevents
overfitting. Applied to face recognition, RS-2DLDA outperformed similar approaches on the MORPH-II and ORL datasets.