Abstract
Image-based facial identity recognition has become a technology that is now used in many applications. This is because it is possible to use only a camera without the need for any other device. Besides, due to the advantage of contactless technology, it is one of the most popular certifications. However, a common recognition system is not possible if some of the face information is lost due to the user’s posture or the wearing of masks, as caused by the recent prevalent disease. In some platforms, although performance is improved through incremental updates, it is still inconvenient and inaccurate. In this paper, we propose a method to respond more actively to these situations. First, we determine whether an obscurity occurs and improve the stability by calculating the feature vector using only a significant area when the obscurity occurs. By recycling the existing recognition model, without incurring little additional costs, the results of reducing the recognition performance drop in certain situations were confirmed. Using this technique, we confirmed a performance improvement of about 1~3% in a situation where some information is lost. Although the performance is not dramatically improved, it has the big advantage that it can improve recognition performance by utilizing existing systems.
Highlights
As image-based user authentication technology advances, face recognition technology is being used in various areas
Because it was an experiment to confirm the difference in recognition performance due to information loss, both images used for verification were partially covered at the same rate
The result was used for a value of 1.0 Equal Error Rate (EER) according to the benchmark protocol
Summary
As image-based user authentication technology advances, face recognition technology is being used in various areas. In addition to improving overall recognition performance, research on robust recognizers continues even when some information is lost due to occlusion [35,36,37,38,39] These studies are developing an identity identification method that corresponds to the case where there is a loss of information due to obscuration, etc., rather than cases where the face is seen completely. This new study has a limitation in that it affects recognition performance in a general environment and needs to train a new recognition network
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