Locating human eye accurately and separating iris and eyelid is of great significance to biometric identification technology, such as iris recognition and face recognition. However, segmentation for the human eye under non-ideal environment is a very challenging job due to lower resolution of eye image, noises like superimposed occlusions of reflections, eyelashes, or shadows. In this study, in view of existing problems for unsheltered human eye image with small pose, we adopted circular Hough transform and morphological algorithm to improve the location of human eye in low resolution. Firstly, this method segments region of interest in face picture with existing face alignment method, and then makes use of bilinear interpolation to remove reflection in image processing module. Secondly, according to the gray distribution of human eye image, we utilize the constrained circular Hough transform to locate iris. Then, combined the global dynamic threshold method, local adaptive threshold method and morphological algorithm are used to locate upper and lower eyelids. After that, fit the eyelid using the least square method so as to cut apart the iris, sclera, upper and lower eyelids in human eye image. Finally, the proposed method was tested on low resolution face image in the wild and UBIRIS V1.0 iris images database released by University of Beira, Portugal. Experimental results demonstrate that the proposed algorithm has strong robustness for human eye location and segmentation in laboratory environment or for low resolution images in the wild.