Heterogeneous Face Recognition Using Kernel Prototype Similarities

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 non-linear kernel similarities to a collection of prototype face images. The prototype subjects 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.