Fusing the line intensity profile and support vector machine for removing reflections in frontal RGB color eye images

Iris recognition is a promising method for accurate identification of a person where the capability of iris segmentation determines its overall performance. Correct iris area has to be determined so that an individual's unique features can be extracted and compared during feature extraction and template matching processes. However, current methods fall short in correctly identifying and classifying reflections in an eye image. This has often led to errors in iris boundary localization and consequently increases the equal error rate in iris recognition. This study thus intends to propose a method that combines a line intensity profile and a support vector machine where the former identifies reflections in eye images, and the latter classifies reflections and non-reflections. The combined method was tested using 1000 eye images from the UBIRISv2 database. Results showed that the combined method provided almost 99.9% classification accuracy. Generally, it has less than 10.5% equal error rate and high decidability index in iris recognition.