Evaluating dynamic texture descriptors to recognize human iris in video image sequence

In the last decades, iris features have been widely used in biometric systems. Because iris features are virtually unique for each person, their usage is highly reliable. However, biometric systems based on iris features are not completely fraud-resistant, as most systems use static images and do not distinguish between a live iris and a photograph. The iris structure and texture change with light variations, and traditional techniques for iris recognition always identify the iris texture in a controlled environment. However, in uncontrolled environments, live irises are recognized by their dynamic response to light: If the light changes, the pupils dilate or contract, and their texture dynamically changes. If a biometric system can identify people during the constriction or dilation time interval, that system will be more fraud-resistant. This paper proposes a new methodology to evaluate the “dynamic texture” from iris image sequences (motion analysis) and measure the discriminant power of these features for biometric system applications. We propose two new dynamic descriptors—dynamic local mapped pattern and dynamic sampled local mapped pattern—which are extensions of the local mapped pattern previously published for texture classification. We applied our proposed dynamic texture descriptors in a sequence of iris images segmented from video under light variation. Then, we compared our results with the well-known dynamic texture descriptor local binary pattern from three orthogonal planes (LBP-TOP). We used statistical measures to evaluate the performance of both descriptors and concluded that our methodology performed better than the LBP-TOP. Moreover, our descriptors can extract dynamic textures faster than the LBP-TOP.