Progress in multi-channel image fusion for face image matching

Fusion techniques have proven to be very useful for many signal and image processing applications including image recognition, image registration, and biometric matching. Along with standard fusion techniques, hypercomplex image processing techniques have been developed recently. These techniques represent a form of image fusion in which several image components are combined to form a multi-channel image. The multi-channel imagery may be processed using hypercomplex transforms, such as the hypercomplex Fourier transform, for image matching and registration. In this paper we investigate performance of multi-channel image fusion for face image matching. We use 3-D color face imagery and investigate fusion of various combinations of grayscale intensity, color, and range information. We conduct a theoretical investigation to identify conditions under which matchers using image channel fusion provide superior matching performance relative to matchers fusing single channel image matching results. We present numerical performance results in the form of Receiver Operating Characteristics curves quantifying matching performance for verification hypothesis testing problems.