Abstract
In many applications, a face recognition model learned on a source domain but applied to a novel target domain degenerates even significantly due to the mismatch between the two domains. Aiming at learning a better face recognition model for the target domain, this paper proposes a simple but effective domain adaptation approach that transfers the supervision knowledge from a labeled source domain to the unlabeled target domain. Our basic idea is to convert the source domain images to target domain (termed as targetize the source domain hereinafter), and at the same time keep its supervision information. For this purpose, each source domain image is simply represented as a linear combination of sparse target domain neighbors in the image space, with the combination coefficients however learnt in a common subspace. The principle behind this strategy is that, the common knowledge is only favorable for accurate cross-domain reconstruction, but for the classification in the target domain, the specific knowledge of the target domain is also essential and thus should be mostly preserved (through targetization in the image space in this work). To discover the common knowledge, specifically, a common subspace is learnt, in which the structures of both domains are preserved and meanwhile the disparity of source and target domains is reduced. The proposed method is extensively evaluated under three face recognition scenarios, i.e., domain adaptation across view angle, domain adaptation across ethnicity and domain adaptation across imaging condition. The experimental results illustrate the superiority of our method over those competitive ones.
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Notes
In probability theory, the support of a probability distribution can be loosely thought of as the closure of the set of possible values of a random variable having that distribution. Here it can be regarded as the closure of the set of all possible instances.
In this experiment the performance of ITL is even worse than PCA, however this does not mean the inferiority of ITL since the data distribution in this setting does not agree with the assumption of ITL: ITL assumes that the data in both source and target domains are tightly clustered, and clusters from both domains are aligned if they correspond to the same class. In this setting here, the source and target domains only have several samples in each class which are difficult to form a tight cluster, and even worse the samples from the source and target domains are from totally different classes.
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Acknowledgments
This work is partially supported by Natural Science Foundation of China under contracts nos. 61025010, 61173065, and 61222211. The authors would like to thank the guest editors and the reviewers for their valuable comments and suggestions. The authors also would like to thank the Edwin Zinan Zeng for his advices about the writing.
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Kan, M., Wu, J., Shan, S. et al. Domain Adaptation for Face Recognition: Targetize Source Domain Bridged by Common Subspace. Int J Comput Vis 109, 94–109 (2014). https://doi.org/10.1007/s11263-013-0693-1
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DOI: https://doi.org/10.1007/s11263-013-0693-1