Cross-database age estimation based on transfer learning
Due to the temporal property of age progression, face images with aging features display some sequential patterns with low-dimensional distributions, which can be effectively extracted by subspace learning algorithms. The patterns extracted by traditional subspace learning methods are mostly restricted to a certain database. As a result, the performance cannot be generalized when applying these patterns to cross databases with different multi-mode variations (e.g. gender, identity, and imaging conditions.) This problem has yet not been given much attention before. In this paper, the cross-database age estimation problem is solved by a transfer learning framework. The proposed framework transfers the knowledge gained from training samples to the target data and improves the performance in cross-database scenarios. Experimental results for age estimation tasks on different datasets demonstrate the effectiveness and robustness of our proposed framework.