研究目的
To propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR) illuminations and contain various types of noise.
研究成果
The proposed pupil localization method provides excellent accuracy and efficiency for the noisy images taken from different CASIA and MMU iris databases. The comparison with the previous methods showed that the proposed method is faster.
研究不足
The method targets the non-close-up and frontal-view NIR images that are captured in the unconstrained environments and have the noise issues as discussed previously. The performance may deteriorate for non-frontal-view iris images.
1：Experimental Design and Method Selection:
The proposed method achieves the pupil localization in the noisy NIR images in two phases: Phase 1: edge-map creation and Phase 2: circle detection using CHT. The objective of Phase 1 is to prepare appropriate input for Phase 2, so that the pupil circle can be detected accurately and rapidly. In Phase 1, the edge-map of the iris images is created using a combination of different image segmentation techniques, which are thresholding, morphological processing, and edge detection. In Phase 2, a general CHT algorithm is implemented to detect a circle in the edge-map by specifying a range of radii as input.
2：Sample Selection and Data Sources:
The proposed method was tested with three iris databases: CASIA-Iris-Thousand (version 4.0), CASIA-Iris-Lamp (version 3.0), and MMU (version 2.0).
3：0), CASIA-Iris-Lamp (version 0), and MMU (version 0).
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials:
MATLAB, version 8.3, installed on a PC with Windows 7 Professional, Intel? Core? i5 CPU @2.40GHz, 8.00GB RAM.
4：3, installed on a PC with Windows 7 Professional, Intel? Core? i5 CPU @40GHz, 00GB RAM.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow:
The eye image is smoothed using a Gaussian filter of size 5×5 and sigma (σ) equal to 1.0. The Sobel edge detector without thinning operation is applied on the smoothed iris image. The intensity-based thresholding is used to segment the pupil region. The image opening operation for black objects is applied on the binary image using a structuring element of type disk of size 7×7. The intersection operation on the two edge-detected images removes the false edges due to the dark illumination. The CHT algorithm is implemented to find the pupil circle radius and the pupil position in the edge-map of the iris image.
5：The Sobel edge detector without thinning operation is applied on the smoothed iris image. The intensity-based thresholding is used to segment the pupil region. The image opening operation for black objects is applied on the binary image using a structuring element of type disk of size 7×The intersection operation on the two edge-detected images removes the false edges due to the dark illumination. The CHT algorithm is implemented to find the pupil circle radius and the pupil position in the edge-map of the iris image.
Data Analysis Methods:
5. Data Analysis Methods:
The accuracy of the proposed method was calculated by inspecting the output images manually after simulation in MATLAB. The time performance was measured using MATLAB’s timer functions 'tic' and 'toc'.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
