An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

Abstract

Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points detected by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. In this paper, we present a combination method of learning-based and edge-based algorithms for iris segmentation. A well-designed Faster R-CNN with only six layers is built to locate and classify the eye. With the bounding box found by Faster R-CNN, the pupillary region is located using a Gaussian mixture model. Then, the circular boundary of the pupillary region is fit according to five key boundary points. A boundary point selection algorithm is used to find the boundary points of the limbus, and the circular boundary of the limbus is constructed using these boundary points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.

Constrained unsteady-state iris fast certification for lightweight training samples based on the scale change stable feature and multi-algorithm voting

“…Iris images(640×480 dimensional) in which the recognition process cannot be completed are eliminated by quality evaluation [22]. The iris region is segmented [23] and mapped to a 512×64 dimensional normalized image by Daugman rubber band normalization [24]. The texture in the normalized image is highlighted by equalizing the histogram [25], and a 256×32 dimensional recognition area is cut from the upper left corner.…”

A new segmentation method for non-ideal iris images using morphological reconstruction FCM based on improved DSA

“…A wide range of segmentation approaches can be found in the literature, which include level set model [15], active contours [16], clustering [19,20], watershed transform [21], graph cut [22], region growing [23], deep learning [24], etc. Among these varieties, clustering is employed for segmentation due to its rapidity and effectiveness.…”

Efficient method for segmentation of noisy and non‐circular iris images using improved particle swarm optimisation‐based MRFCM

“…Optimisation based on hybrid soft computing called 'adaptive fuzzy genetic algorithm is used for both unimodel and multi-model biometric authentication systems by combining the iris and fingerprint biometrics. Li et al [21] proposed a segmentation method that combines learning-based and edge-based algorithms. To locate an eye region in an image faster region-convolution neural networks is employed.…”

“…Then, Gaussian mixture model and boundary point selection algorithms are employed to detect pupil and limbus boundaries, respectively. Some other algorithms that have been proposed in the literature for segmentation of iris images captured under non-cooperative conditions [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

“…Li et al . [21] proposed a segmentation method that combines learning‐based and edge‐based algorithms. To locate an eye region in an image faster region‐convolution neural networks is employed.…”