A novel retina-based human identification algorithm based on geometrical shape features using a hierarchical matching structure. (April 2017) - Nazari, Pouya; Pourghassem, Hossein
Highlights: We propose a rotation-invariant retina-based human identification algorithm. We define novel geometrical features based on surrounded regions by blood vessels. The retinal images are enrolled in database by their surrounded regions. We employ a hierarchical matching structure to match surrounded regions of images. Matched surrounded regions of query with enrolled items identify or reject the query. Abstract: Background and Objectives: Retinal image is one of the most secure biometrics which is widely used in human identification application. This paper represents a rotation and translation-invariant human identification algorithm based on a new definition of geometrical shape features of the retinal image using a hierarchical matching structure. Methods: In this algorithm, the retinal images are represented by regions which are surrounded by blood vessels that are named Surrounded Regions (SRs). A perfect set of region-based and boundary-based features are defined on the SRs. In the boundary-based features, by defining corner points of the SR, novel features such as angle of SR corner, centroid distance and weighted corner angle are defined which they can describe well the variation rate of boundary and geometry of the SR. To match the SR of a query with enrolled SR in database, the extracted features in a hierarchical structure from simpler features through more complex features are applied to filter the enrolled SRs in the database for search space reduction. At last, the matched candidate SRs with the query SRs determine the identification or rejection of query image by proposed decision making scenario. In this scenario, the identification is carried out when at least two SRs of the query are matched with two SRs of an individual in the database. Results: The proposed algorithm is evaluated on STARE and DRIVE retinal image databases in six different experiments and is achieved an accuracy rate of 100% and an average processing time of 3.216 sec and 3.225 sec, respectively. The reported results demonstrate the efficiency of our proposed algorithm in the eye-movement condition. Conclusion: In our work, by defining the SR-based features and employing a hierarchical matching structure, the computational complexity of matching step is reduced and also the identification performance is improved. Moreover, the proposed algorithm overcomes the problem of natural movements of the head and eye during the capturing process.