Affiliation:
1. Departamento Tecnologia Electronica, ETSI Telecomunicacion, University of Málaga, 29071 Málaga, Spain
Abstract
One of the main challenges faced by iris recognition systems is to be able to work with people in motion, where the sensor is at an increasing distance (more than 1 m) from the person. The ultimate goal is to make the system less and less intrusive and require less cooperation from the person. When this scenario is implemented using a single static sensor, it will be necessary for the sensor to have a wide field of view and for the system to process a large number of frames per second (fps). In such a scenario, many of the captured eye images will not have adequate quality (contrast or resolution). This paper describes the implementation in an MPSoC (multiprocessor system-on-chip) of an eye image detection system that integrates, in the programmable logic (PL) part, a functional block to evaluate the level of defocus blur of the captured images. In this way, the system will be able to discard images that do not have the required focus quality in the subsequent processing steps. The proposals were successfully designed using Vitis High Level Synthesis (VHLS) and integrated into an eye detection framework capable of processing over 57 fps working with a 16 Mpixel sensor. Using, for validation, an extended version of the CASIA-Iris-distance V4 database, the experimental evaluation shows that the proposed framework is able to successfully discard unfocused eye images. But what is more relevant is that, in a real implementation, this proposal allows discarding up to 97% of out-of-focus eye images, which will not have to be processed by the segmentation and normalised iris pattern extraction blocks.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference34 articles.
1. Long range iris recognition: A survey;Nguyen;Pattern Recognit.,2017
2. Accurate Iris Recognition at a Distance Using Stabilized Iris Encoding and Zernike Moments Phase Features;Tan;IEEE Trans. Image Process.,2014
3. Real-time embedded eye detection system;Pedraza;Expert Syst. Appl.,2022
4. A Local Metric for Defocus Blur Detection Based on CNN Feature Learning;Zeng;IEEE Trans. Image Process.,2019
5. An FPGA-Based Energy-Efficient Reconfigurable Convolutional Neural Network Accelerator for Object Recognition Applications;Li;IEEE Trans. Circuits Syst. II Express Briefs,2021
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献