3D iris model and reader for iris identification. (6th January 2020)

• Record Type: Journal Article
• Authors: Cohen, Fernand; Sowmithran, Sowrirajan; Li, Chenxi
• Is Part Of: Concurrency and computation. Volume 33:Number 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-06
• Subjects: 3D high‐resolution iris -- 3D iris identification -- 3D iris reader -- appearance texture -- loop subdivision -- salient features; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5653
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In the presence of eyelids and eyelashes movement, pupil dilation, poor lighting, blur due to movement during iris image acquisition, and factors that collectively cause distortion in the iris image, 2D image‐based iris identification techniques become limited and might lead to iris misclassification due to the dependency on the image appearance (texture) on the possibly distorted image. To alleviate this problem, we introduce a new 3D iris model and reader based upon which iris identification is performed. Using a set of at least two 2D images taken from different views, a small set of reliable and corresponding salient fiducial points (corner points taken from crypts, corona, and serpentine rings of iris pattern) in the two images are extracted, from which a set of 3D iris salient points are obtained using triangulation. Corresponding salient points in the 2D images are found using the Random Sampling Consensus (RANSAC) algorithm, which is robust in identifying the inlier points that correspond to each other in different views of the iris. From this small and reliable salient point set, a denser (high‐resolution) set of extra salient feature points is constructed at minimum cost using a loop subdivision method that yields corresponding extra salient points in the two images from which a high‐resolution 3D iris model is obtained. The points associated with the 3D high‐resolution model are tessellated to form a high‐resolution triangular mesh with the appearance of a triangular patch in the image imported onto the iris personalized 3D model rendering it a 3D iris surface with appearance. This 3D model construction method allows for a 3D classification of a test iris to one of many possible irises stored in the database. The classification is based on a geometric 3D point cloud error. For cross‐validation, the geometric‐based classification can be augmented by considering a mean squared error (MSE) based on the appearance of corresponding points on the test and base models to further disambiguate between irises with close geometric error values.