Detecting Eyelash and Reflection for Accurate Iris Segmentation

Abstract

Accurate iris segmentation is presented in this paper, which is composed of two parts, reflection detection and eyelash detection. Eyelashes are classified into two categories, separable and multiple. An edge detector is applied to detect separable eyelashes, and intensity variances are used to recognize multiple eyelashes. Reflection is also divided into two types, strong and weak. A threshold and statistical model is proposed to recognize the strong and weak reflection, respectively. We have developed an iris recognition approach for testing the effectiveness of the proposed segmentation method. The results show that the proposed method can reduce recognition error for the iris recognition approach.

Personal Identity Recognition Approach Based on Iris Pattern

“…In iris images captured, interference, such as eyelids, eyelash and facula, will affect iris effective information for iris recognition [Wai-Kin Kong & David Zhang, 2003], we must remove these interferences between pupil boundary and iris outer boundary. From image #1 to image #4, we can see that eyelids cover iris's upper part and lower part usually.…”

Segmentation and Edge Detection Based on Modified ant Colony Optimization for Iris Image Processing

“…There are no universal solutions for image segmentation. However, for specific classes of segmentation problems many good solutions have been developed [11,12]. In some methods localizing pupil has been developed with wavelet transform (WT), and iris boundary extracted with a differential integral operator [13].…”

“…In some methods localizing pupil has been developed with wavelet transform (WT), and iris boundary extracted with a differential integral operator [13]. In another approach, binary morphology and local statistics have been applied in iris segmentation [11]. Geodesic Active Contours (GACs) also has been developed in iris image segmentation [4].…”

Non-ideal iris segmentation using graph cuts

“…To this end, Ma et al [13] use Fourier transforms to determine whether the iris is being occluded by the eyelashes; the unique spectrum associated with eyelashes are used to reject images in which significant iris occlusion occurs. Other approaches for eyelash segmentation involve the use of image intensity differences between the eyelash and iris regions [12,11], gray level co-occurrence matrices [1], and the use of multiple eyelash models [16].…”