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Face recognition using a near-infrared (NIR) sensor is widely applied to practical applications such as mobile unlocking or access control. However, unlike RGB sensors, few deep learning approaches have studied NIR face recognition. We conducted comparative experiments for the application of deep learning to NIR face recognition. To accomplish this, we gathered five public databases and trained two deep learning architectures. In our experiments, we found that simple architecture could have a competitive performance on the NIR face databases that are mostly composed of frontal face images. Furthermore, we propose a data augmentation method to train the architectures to improve recognition of users who wear glasses. With this augmented training set, the recognition rate for users who wear glasses increased by up to 16%. This result implies that the recognition of those who wear glasses can be overcome using this simple method without constructing an additional training set. Furthermore, the model that uses augmented data has symmetry with those trained with real glasses-wearing data regarding the recognition of people who wear glasses.

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Vision-based gaze trackers that utilize corneal reflections of IR lights are vulnerable to users wearing glasses. IR reflection on eye glasses overlap with pupil region to interfere gaze estimation. This overlap problem should be resolved properly when the moving region of user is relatively large. In this paper, we propose a solution to handle the overlapping problem in gaze tracking for users wearing glasses. One of the two IR sources will be turned off when it becomes a problematic glint on the glasses. We propose a model that calculates gaze point with the remaining single glint. The proposed method was implemented on our state-of-the-art gaze tracker. The tracked results showed that our proposed model with the remaining glint preserved the accuracy of the system.

The importance of an automated defect inspection system has been increasing in the manufacturing industries. Various products to be examined have periodic textures. Among image-based inspection systems, it is common that supervised defect segmentation requires a great number of defect images with their own region-level labels; however, it is difficult to prepare sufficient training data. Because most products are of normal quality, it is difficult to obtain images of product defects. Pixelwise annotation for semantic segmentation tasks is an exhausting and time-consuming process. To solve these problems, we propose a weakly-supervised defect segmentation framework for defect images with periodic textures and a data augmentation process using generative adversarial networks. With only imagelevel labeling, the proposed segmentation framework translates a defect image into its defect-free version, called a golden template, using CycleGAN and then segments the defects by comparing the two images. The proposed augmentation process creates whole new synthetic defect images from real defect images to obtain sufficient data. Furthermore, synthetic non-defect images are generated even from real defect images through the augmentation process. The experimental results demonstrate that the proposed framework with data augmentation outperforms an existing weakly-supervised method and shows remarkable results comparable to those of supervised segmentation methods. INDEX TERMS Automated defect inspection, visual inspection system, weakly-supervised learning, periodic textures, data augmentation, generative adversarial networks.

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