Eye side and orientation detection of iris images using lightweight textural descriptors for embedded systems

Iris recognition is a widely used biometric authentication technique due to its high accuracy and uniqueness. However, these systems are vulnerable to spoofing attacks, which can occur by rotating an iris image or an iris scanner during image acquisition. Additionally, correctly recognizing the eye side significantly reduces computational load and decreases the likelihood of false positives in biometric systems. This paper introduces a novel method for automatically detecting the correct left/right and upright/upside-down orientation of an iris image. The proposed method employs a lightweight feature extraction algorithm utilizing Local Binary Pattern (LBP) and Gray-Level Co-Occurrence Matrix (GLCM) to extract features from the iris image. A Support Vector Machine (SVM) classifier is then used to determine the eye side or orientation of the iris images. LBP captures the local texture details, whereas GLCM describes the statistical features of the iris images. By combining these textural features, the proposed method improves the ability to classify eye side or orientation. The efficient and precise texture descriptors allow for implementation on embedded systems, including IoT or mobile devices. Experimental results on benchmark datasets demonstrate that the proposed method outperforms existing baseline methods in both performance and speed.