A less intrusive iris capturing system usually requires a long stand-off distance, large capture volume, and no restriction of a static subject. These factors make iris recognition more challenging than that from today's close-range iris systems. In this paper we propose a novel algorithm toward robust iris recognition in less intrusive environments. Our algorithm consists of two parts: 1) a novel iris segmentation method that can handle variable resolutions (from 50 pixels to 350 pixels), lighting, and partial occlusion, which can cause the majority of pixels or edges in a captured image are outliers. 2) a new feature encoding method that is robust for non-ideal iris images due to noise, blur, occlusion, and down-sampling. Through a careful analysis of the iris image acquisition process and extensive simulation, we show that, contrary to the common belief that iris diameter has a significant impact on recognition accuracy, it is the image noise that reduces accuracy in low resolution images when an accurate segmentation can be obtained. Using high-quality low noise images acquired by digital SLR cameras, we showed that our iris recognition algorithm can achieve state-of-the-art performance (e.g., FRR at 0.0015 with FAR 0.001) on very low resolution images with iris diameter around 60 pixels.