Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics

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.

FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance System

“…If we decrease this size to 320 × 320, the system is able to process 44 fps, a value very close to the frames per second provided by the sensor. In previous work, the sensor input image was rescaled to a size of 256 × 256 pixels [15,50]. With this size, for example, these previous implementations using a modified Viola Jones classifier achieved 100% positive detections in the CASIA-Iris-Distance v4 database.…”

“…The problem is that the system provides many false positives, which saturate the subsequent recognition module. Many of these false positives contain out-of-focus eyes, which could be discarded by a blur estimation module [15], but others are associated with regions that do not contain eyes. The biggest problem with this approach lies in scalability and adaptability.…”

“…Although the images are captured, and stored in DDR memory, in their original size, for processing they are reduced to a size of 256 × 256 pixels. Previous work has shown that eyes are accurately detected despite this reduction in resolution [15,50]. These scaled images are made available to the DPU using VDMA.…”

“…As intended by this proposal, this allows it not only to detect eyes, but also to discard those that are not in focus. In a system that works with moving people, where up to 250 eye images can be captured in 2-3 s, being able to discard invalid eye images is really necessary [15]. In this implementation, it will therefore not be necessary to include a defocus blur estimation module in the system.…”

“…One solution to this problem is to filter out the large number of images in which the iris is not in focus. This means discarding almost 97% of the eye images captured by the system [15]. In this paper, we focus on one of the first steps of the iris recognition identification system: the detection of the eye/iris image.…”