Heterogeneous face recognition (HFR) involves matching two face images from alternate imaging modalities, such as an infrared image to a photograph or a sketch to a photograph. Accurate HFR systems are of great value in various applications (e.g., forensics and surveillance), where the gallery databases are populated with photographs (e.g., mug shot or passport photographs) but the probe images are often limited to some alternate modality. A generic HFR framework is proposed in which both probe and gallery images are represented in terms of nonlinear similarities to a collection of prototype face images. The prototype subjects (i.e., the training set) have an image in each modality (probe and gallery), and the similarity of an image is measured against the prototype images from the corresponding modality. The accuracy of this nonlinear prototype representation is improved by projecting the features into a linear discriminant subspace. Random sampling is introduced into the HFR framework to better handle challenges arising from the small sample size problem. The merits of the proposed approach, called prototype random subspace (P-RS), are demonstrated on three different heterogeneous scenarios: 1) near-infrared (NIR) to photograph, 2) thermal to photograph and, 3) classic to photograph.

This Matlab design approaches a unified approach to heterogeneous face recognition that,
    1. Achieves leading accuracy on multiple HFR scenarios,
    2. Does not necessitate feature descriptors that are invariant to changes in image modality,
    3. Facilitates recognition using different feature descriptors in the probe and gallery modalities, and
    4. Naturally extends to additional HFR scenarios due to properties 2 and 3 above.

Reference Paper: Heterogeneous Face Recognition Using Kernel Prototype Similarities
Author’s Name:  Brendan F. Klare, and Anil K. Jain
Source: IEEE

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