Thermal and Visible Image Fusion for Ear Recognition

Abstract

This paper discusses the possibility of fusing thermal and visible images to improve ear recognition ability in images of varying illuminations. Since thermal image is known to remain invariant to lighting changes and visible images are able to capture feature details, image fusion is proposed to accentuate the strengths of both spectra for ear recognition. Two popular image fusion techniques, weighted average (WA) and discrete wavelet transform (DWT) are used as a preliminary investigation. Eigenvectors are extracted from the fused image and recognition is performed using metric distance measure. With 67.5% recognition rate, DWT fused images performed better than WA fused images (63.75%). Thermal images, on the other hand, achieved 68.75% recognition rate. Even though thermal images performed slightly better than DWT fused images by 1.25%, the difference is deemed as insignificance due to the small dataset used and the primitive fusion rules employed. Further studies on the fusion techniques need to be done to improve fusion method.