access icon free Heterogeneous face recognition based on modality-independent Kernel Fisher discriminant analysis joint sparse auto-encoder

A novel method called modality-independent Kernel discriminant analysis joint sparse auto-encoder, for solving heterogeneous face recognition problem is proposed. A projection matrix to map multimodal data into a common feature space for representing cross-modal image data is first learnt. Then extend the model via sparse auto-encoder in an unsupervised manner with the combination of a regularisation term and a Kullback–Leiber divergence term. Different from classical approaches, this model does not require the data correspondences when collecting external cross-modal data. Thus, it is practical for real-world cross-modal classification problem. Experiments conducted on two heterogeneous face datasets demonstrate the effectiveness of the proposed approach.

Inspec keywords: image coding; image classification; matrix algebra; image representation; face recognition

Other keywords: Kullback–Leiber divergence term; multimodal data map; cross-modal image data representation; common feature space; projection matrix; joint sparse auto-encoder; heterogeneous face recognition; modality-independent kernel Fisher discriminant analysis; cross-modal classification

Subjects: Image and video coding; Image recognition; Algebra; Algebra; Computer vision and image processing techniques

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