Probabilistic Linear Discriminant Analysis for intermodality face recognition

Intermodality face matching or Heterogeneous face recognition involves matching faces from different modalities such as infrared images, sketch images and low/high resolution visual images. This problem is further alleviated due to inherit problems in face recognition such as pose, expression, illumination, occlusion etc. Existing face recognition algorithms fail to address the existing feature gap exist between images of different modalities. To solve this problem, we propose a new method inspired from Probabilistic Linear Discriminant Analysis (PLDA). PLDA is a generative probabilistic method which models the face into signal and noise components. This method reports outstanding results when compared to other contemporary approaches. But PLDA is designed to apply the image data in only one modality. In this paper, its efficacy has been extended to more generic problem of handling faces captured in different modalities. Experiments conducted on HFB (VIS-NIR), Biosecure (Low-High or Webcam-Digitalcam) face databases validate its robustness and superiority over other methods.