Coupled dictionaries for thermal to visible face recognition
Thermal to visible face recognition is the problem of identifying a thermal infrared (IR) face image given a gallery of visible light face images. We attempt to solve this problem by learning coupled dictionaries to represent the two domains. The dictionaries provide a sparse representation which transforms the data into a single, domain-independent, latent space. We formulate the dictionary learning problem as a bi-level optimization problem and perform a stochastic gradient descent on the dictionaries to solve it. We present experimental results demonstrating the effectiveness of our approach.