For iris recognition, we employ an algorithm that extracts features using domain-specific, human-inspired Binarized Statistical Image Features (BSIF) and performs matching with mean fractional Hamming Distance. The presentation attack detection module is based on the best-performing methods among all available open-source iris PAD algorithms evaluated to date. We use an image-complexity guided network compression scheme, CC-Net, to design a lightweight iris segmentation network that operates with high speed on Raspberry Pi, while producing high-quality masks. Our open-source software pipeline consists of three modules: segmentation, presentation attack detection, and recognition. The total cost of the hardware in the moment of writing this paper was less than 75 USD, which was cheaper than most commercial iris systems. We designed our hardware in a way such that it is low-cost, easy to assemble, easy to use, and follows the ISO recommendations regarding the iris image capture (thus the use of NIR, rather than visible light). In addition to the main Pi board, the hardware involves a NIR-sensitive Pi camera, an NIR-filter, two NIR LEDs, a breadboard, resistors, and jumper wires. The hardware platform is based on the Raspberry Pi 3B+ board, through which we perform illumination control, image acquisition, and the entire image processing involved in the pipeline. In this paper, we propose an open source iris recognition system (hardware and software) with presentation attack detection (PAD). The hardware specifications and all source codes of theĮntire pipeline are made available along with this paper. Runs in about 3.2 seconds and the proposed PAD runs in about 4.5 seconds on Segmentation, domain-specific human-inspired Binarized Statistical Imageįeatures (BSIF) to build an iris template, and to combine 2D (iris texture) andģD (photometric stereo-based) features for PAD. We propose a lightweight imageĬomplexity-guided convolutional network for fast and accurate iris Low-cost secure iris recognition alternative to more sophisticated systems, and Spoof-resistant iris recognition, which may (a) stimulate research in iris PADĪnd allow for easy prototyping of secure iris recognition systems, (b) offer a The primary goal of this work is to offer a low-cost baseline for Iris recognition system with presentation attack detection ( PAD), which can beĮasily assembled for about 75 USD using Raspberry Pi board and a few This paper proposes the first known to us open source hardware and software

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