Fusion of Siamese Network‐Based Sclera and Iris Detection: A Multimodal Biometrics Approach Using a Sclera Detection Tracing Algorithm

Traditional security methods need to be improved as a result of security difficulties over time. Biometrics was introduced as a result of this. The sclera has been an area of extensive study recently as far as biometrics is concerned. This is because it is accurate; nevertheless, the application of this biometric feature has been limited by its segmentation. It is still necessary to improve segmentation accuracy even though several techniques have been published in the literature. This study recommends using a sclera detection tracing (SDT) approach in conjunction with the circular Hough transform. Additionally, a system based on the discrete wavelet transform (DWT) fusion of local binary‐based features of the iris and sclera was proposed by the study. The fusion was passed to a Siamese network for classification. A comparison between the outcomes of the unimodal and bimodal systems was conducted. The result showed that the best performance of 98.5 was obtained for the fusion of the two biometrics. Likewise, the sclera result based on the sclera detection algorithm performed better than the segmentation that was done with the convolutional neural network (CNN).