Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

Owing to the rapid advancements in smartphone technology, there is an emerging need for a technology that can detect banknotes and coins to assist visually impaired people using the cameras embedded in smartphones. Previous studies have mostly used handcrafted feature-based methods, such as scale-invariant feature transform or speeded-up robust features, which cannot produce robust detection results for banknotes or coins captured in various backgrounds and environments. With the recent advancement in deep learning technology, some studies have been conducted on banknote and coin detection using a deep convolutional neural network (CNN). However, these studies also showed degraded performance depending on the changes in background and environment. To overcome these drawbacks, this paper proposes a three-stage detection technology for new banknotes and coins by applying faster region-based CNN, geometric constraints, and the residual network (ResNet). In the experiment performed using the open database of Jordanian dinar (JOD) and 6,400 images of eight types of Korean won banknotes and coins obtained using our smartphones, the proposed method exhibited a better detection performance than the state-of-the-art methods based on handcrafted features and deep features.

In recent years, the importance of semantic segmentation has been widely recognized and the field has been actively studied. The existing state-of-the-art segmentation methods show high performance for bright and clear images. However, in low light or nighttime environments, images are blurred and noise increases due to the nature of the camera sensor, which makes it very difficult to perform segmentation for various objects. For this reason, there are few previous studies on multi-class segmentation in low light or nighttime environments. To address this challenge, we propose a modified cycle generative adversarial network (CycleGAN)-based multi-class segmentation method that improves multi-class segmentation performance for low light images. In this study, we used low light databases generated by two road scene open databases that provide segmentation labels, which are the Cambridge-driving labeled video database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) database. Consequently, the proposed method showed superior segmentation performance compared with the other state-of-the-art methods.

Visible light surveillance cameras are currently deployed on a large scale to prevent crime and accidents in public urban environments. For this reason, various human identification studies using biometric data are underway in surveillance environments. The most active research area is face recognition, which generally shows excellent performance; however, aging, changes in facial expression, and occlusions by accessories cause a rapid decline in recognition performance. To resolve these problems, we propose a periocular recognition method in surveillance environments that is based on the convolutional neural network. In this paper, experiments were performed using the custom-made Dongguk periocular database and the open database of ChokePoint database. It was confirmed that the proposed method performs better than existing techniques used in periocular recognition. It was also found to perform better than conventional techniques in face recognition when an occlusion is present.

In recent years, the importance of the semantic segmentation field has been increasingly emphasized because autonomous vehicle and artificial intelligence (AI)-based robot technology are being researched extensively; and methods for accurately recognizing objects are required. Previous state-of-the-art segmentation methods have been proven to be effective for databases obtained during daytime. However, in extremely low light or nighttime environments, the shape and color information of objects are very small or disappear due to an insufficient amount of external light, which makes it difficult to train the segmentation network and significantly degrades performance. In our previous work, segmentation performance in a low light environment was improved using the enhancement-based segmentation method. However, low light images could not be restored precisely and segmentation performance improvement was limited because only per-pixel loss functions were used when training the enhancement network. To overcome these drawbacks, we propose a low light image segmentation method based on a modified perceptual cycle generative adversarial network (CycleGAN). Perceptual image enhancement was performed using our network, which significantly improved segmentation performance. Unlike the existing perceptual loss, the Euclidean distance of the feature maps extracted from the pretrained segmentation network was used. In our experiments, we used low light databases generated from two famous road scene open databases, which are Cambridge-driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI), and confirmed that our proposed method shows better segmentation performance in extremely low light environments than the existing state-of-the art methods.