Iris image retrieval using partial matching of image blocks

Abstract

Abstract The problem of human identification through recognition of patterns in iris images captured in unconstrained environments results in image artefacts such as image occlusion and specular reflection, in which iris tissue is observable to extremely low content. To overcome this problem, this paper presents a novel method for iris image retrieval and recognition based on partial pattern matching. The main contribution of the proposed method relies on an image partitioning schema in which the iris is divided into non‐overlapping blocks with varying dimensions, facilitating the identification and removal of image regions impaired by eyelids, eyelashes, and specular reflections. In fact, the blocks that contain artefacts are completely ignored and those blocks are preserved that include useful patterns for identification. In addition, a multi‐feature similarity followed by a score fusion technique is employed for ranking the retrieval results. The remarkable results of the classification stage include an accuracy of 100%, 98.75%, and 99.94% on three benchmark databases, including UPOL, UBIRIS.V2, and CASIA‐Iris‐Interval.v3, respectively. Additionally, in the retrieval stage, the proposed method achieves a precision of 100% on all three benchmark databases, a recall of 83.33%, and a F1‐measure of 90.91 on the CASIA‐Iris‐Interval.v3 dataset.

Keywords

• biometrics (access control)
• convolutional neural nets
• image retrieval
• iris recognition
• pattern classification