A ResourceEfficient Embedded Iris Recognition System Using Fully Convolutional Networks Applications of Fully Convolutional Networks (FCN) in iris segmentation have shown promising advances. For mobile and embedded systems, a significant challenge is that the proposed FCN architectures are extremely computationally demanding. In this article, we propose a resourceefficient, endtoend iris recognition flow, which consists of FCNbased segmentation, contour fitting, followed by Daugman normalization and encoding. To attain accurate and efficient FCN models, we propose a threestep SWHW codesign methodology consisting of FCN architectural exploration, precision quantization, and hardware acceleration. In our exploration, we propose multiple FCN models, and in comparison to previous works, our bestperforming model requires 50X less FLOPs per inference while achieving a new stateoftheart segmentation accuracy. Next, we select the most efficient set of models and further reduce their computational complexity through weights and activations quantization using 8bit dynamic fixedpoint (DFP) format. Each model is then incorporated into an endtoend flow for true recognition performance evaluation. A few of our endtoend pipelines outperform the previous stateoftheart on two datasets evaluated. Finally, we propose a novel DFP accelerator and fully demonstrate the SWHW codesign realization of our flow on an embedded FPGA platform. In comparison with the embedded CPU, our hardware acceleration achieves up to 8.3X speedup for the overall pipeline while using less than 15% of the available FPGA resources. We also provide comparisons between the FPGA system and an embedded GPU showing different benefits and drawbacks for the two platforms.

Iris Feature Extraction and Matching Method for Mobile Biometric Applications Biometric methods are increasingly penetrating the field of mobile applications, confronting researchers with a huge number of problems that have not been considered before. Many different interaction scenarios in conjunction with the mobile device performance limitations challenge the capabilities of onboard biometrics. Saturated with complex textural features the iris image is used as a source for the extraction of unique features of the individual that are used for recognition. The mentioned features inherent to the interaction with the mobile device affect not only the source image quality but natural deformations of the iris leading to high intraclass variations and hence reducing the recognition performance. A novel method for iris feature extraction and matching is represented in this work. It is based on a lightweight CNN model combining the advantages of a classic approach and advanced deep learning techniques. The model utilizes shallow and deep feature representations in combination with characteristics describing the environment that helps to reduce intraclass variations and as a consequence the recognition errors. It showed high efficiency on the mobile and a few more datasets outperforming stateoftheart methods by far.