scholarly journals Recognition of Damaged Arrow-Road Markings by Visible Light Camera Sensor Based on Convolutional Neural Network

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2160 ◽  
Author(s):  
Husan Vokhidov ◽  
Hyung Hong ◽  
Jin Kang ◽  
Toan Hoang ◽  
Kang Park
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Camera Sensor

scholarly journals Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 410 ◽  
Author(s):  
Dat Nguyen ◽  
Tuyen Pham ◽  
Min Lee ◽  
Kang Park
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Spatial Information ◽  
Recognition System ◽  
Image Features ◽  
Attack Detection ◽  
Temporal Information ◽  
Public Datasets ◽  
Presentation Attack Detection ◽  
Camera Sensor

Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.

Visible light polarization image desmogging via Cycle Convolutional Neural Network

2021 ◽  
Author(s):  
Jingjing Zhang ◽  
Xin Zhang ◽  
Teng Li ◽  
Yuzhou Zeng ◽  
Gang Lv ◽  
...  
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Light Polarization ◽  
Polarization Image

scholarly journals Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2296 ◽  
Author(s):  
Wan Kim ◽  
Jong Min Song ◽  
Kang Ryoung Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Image Acquisition ◽  
Multimodal Biometrics ◽  
Finger Vein ◽  
Nir Light ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Camera Sensor

Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.

Winter Road Surface Conditions Classification using Convolutional Neural Network (CNN): Visible Light and Thermal Images Fusion

2021 ◽  
Author(s):  
Ce Zhang ◽  
Ehsan Nateghinia ◽  
Luis Miranda-Moreno ◽  
Lijun Sun
Keyword(s):  
Neural Network ◽  
Visible Light ◽  
Convolutional Neural Network ◽  
Weighted Average ◽  
Road Surface ◽  
Thermal Images ◽  
Winter Maintenance ◽  
Surface Conditions ◽  
Snow Plowing ◽  
Winter Road

In winter, road conditions play a crucial role in traffic flow efficiency and road safety. Icy, snowy, slushy, or wet road conditions reduce tire friction and affect vehicle stability which could lead to dangerous crashes. To keep traffic operations safe, cities spend a significant budget on winter maintenance operations such as snow plowing and spreading salt/sand. This paper proposes a methodology for automated winter road surface conditions classification using Convolutional Neural Network and the combination of thermal and visible light cameras. As part of this research, 4,244 pairs of visible light and thermal images are captured from pavement surfaces and classified into snowy, icy, wet, and slushy surface conditions. Two single-stream CNN models (visible light and thermal streams), and one dual-stream CNN model are developed. The average F1-Score of dual-stream model is 0.866, 0.935, 0.985, and 0.888 on snowy, icy, wet, and slushy, respectively. The weighted average F1-Score is 0.94.

Sign in / Sign up

Export Citation Format

Share Document