Locality Versus Globality: Query-Driven Localized Linear Models for Facial Image Computing
Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonstrate in this paper that applying the local manner in sample space, feature space, and learning space via linear subspace learning can sufficiently boost the discriminating power, as measured by discriminating power coefficient (DPC). The proposed solution achieves good classification accuracy gains and shows computationally efficient. Particularly, we approximate the global nonlinearity through a multimodal localized piecewise subspace learning framework, in which three locality criteria can work individually or jointly for any new subspace learning algorithm design. It turns out that most existing subspace learning methods can be unified in such a common framework embodying either the global or local learning manner. On the other hand, we address the problem of numerical difficulty in the large-size pattern classification case, where many local variations cannot be adequately handled by a single global model. By localizing the modeling, the classification error rate estimation is also localized and thus it appears to be more robust and flexible for the model selection among different model candidates. As a new algorithm design based on the proposed framework, the query-driven locally adaptive (QDLA) mixture-of-experts model for robust face recognition and head pose estimation is presented. Experiments demonstrate the local approach to be effective, robust, and fast for large size, multiclass, and multivariance data sets.