This paper proposes a compact face representation for face recognition. The face with landmark points in the image is detected and then used to generate transformed face regions. Different types of regions form the transformed face region datasets, and face networks are trained. A novel forward model selection algorithm is designed to simultaneously select the complementary face models and generate the compact representation. Employing a public dataset as training set and fusing by only six selected face networks, the recognition system with this compact face representation achieves 99.05% accuracy on LFW benchmark.
CITATION STYLE
Shao, W., Wang, H., Zheng, Y., & Ye, H. (2016). Compact face representation via forward model selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9967 LNCS, pp. 112–120). Springer Verlag. https://doi.org/10.1007/978-3-319-46654-5_13
Mendeley helps you to discover research relevant for your work.