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Found 4 papers, 1 papers with code
Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described.
Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation
The initial and paramount step for performing this type of recognition is the segmentation of the region of interest, i. e. the sclera.
A Benchmark for Iris Location and a Deep Learning Detector Evaluation
The iris is considered as the biometric trait with the highest unique probability.
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
First, in the SSIG dataset, composed of 2, 000 frames from 101 vehicle videos, our system achieved a recognition rate of 93. 53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89. 80% and 93. 03%, respectively) and considerably outperforming previous results (81. 80%).
Ranked #2 on
License Plate Recognition
on SSIG-SegPlate
