Liveness detection for iris recognition using multispectral images

Liveness detection is a necessary step towards higher reliability of iris recognition. In this research, we propose a novel iris liveness detection method based on multi-features extracted from multispectral images. First, we analyze the specific multispectral characteristics of conjunctival vessels and iris textures. To ensure the effective utilization of these characteristics, iris images are simultaneously captured at near-infrared (860 nm) and blue (480 nm) wavelengths. Then we respectively define and measure relative number of conjunctival vessels (RNCV) and entropy ratio of iris textures (ERIT) using 860-nm and 480-nm images. Finally, the feature values of RNCV and ERIT are arranged to form a robust 2-D feature vector. The trained Support Vector Machine (SVM) is used to classify the feature vectors extracted from live and fake irises. Experimental results demonstrate that the proposed method can discriminate between live irises and various types of fake irises with high classification accuracy and low computational cost.

► The 860 nm and 480 nm iris images are exploited to detect liveness.
► The relative number of conjunctival vessels is used as a robust feature.
► The entropy ratio of iris textures is computed as a liveness indicator.
► A multispectral iris database is developed.