Infrared Face Recognition Based on Radiant Energy and Curvelet Transformation

In this paper, a infrared face recognition method using radiant energy conversion and curvelet transformation is proposed. Firstly, to get the stable feature of thermal face, thermal images are converted into radiant energy images according to Stefan-Boltzmann´s law. Secondly, curvelet transform has better directional and edge representation abilities than widely used wavelet transformation and other classic transformations. Inspired by these attractive attributes of curvelets in sparse representation of the images, we introduce the idea of decomposing images into their curvelet subbands to extract the principal representative feature, which saves the computational complexity and storage units. Finally, the nearest neighbor classifier is chosen to get the system recognition result. The experiments illustrate that compared with traditional PCA based systems, the proposed system has better performance and requires fewer computations and memory units.