In this paper, we propose a new concept for human identification (ID): sclera recognition. The blood vessel structure of the sclera is unique to each person, and it can be remotely obtained nonintrusively in the visible wavelengths. Therefore, it is well suited for human identification (ID). The vessel structure in the sclera is multilayered and has complex nonlinear deformations. Contributions in this paper are: 1. We proposed the new approach for human ID: sclera recognition. 2. We developed a new method for sclera segmentation which works for both color and grayscale images. 3. We designed a Gabor wavelet-based sclera pattern enhancement method to emphasize and binarize the sclera vessel patterns. 4. We proposed a line-descriptor based feature extraction, registration, and matching method that are illumination, scale, orientation, and deformation invariant and can mitigate the multilayered deformation effects and tolerate segmentation error.