A Novel Infrared and Visible Image Fusion Approach Based on Adversarial Neural Network

Chen, Xianglong; Wang, Haipeng; Liang, Yaohui; Meng, Ying; Wang, Shifeng

doi:10.3390/s22010304

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…The use of multiple spectral bands improves the performance of facial recognition system, this explains the interest of researchers on MS facial recognition in recent years [5] [10] [11]. A face recognition system can be represented by four main modules: capture, feature extraction, matching and decision.…”

Section: Related Workmentioning

confidence: 99%

Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

HOUSSOU¹,

Mahama²,

Gouton³

et al. 2022

IJACSA

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Face recognition in the visible and Near Infrared range has received a lot of attention in recent years. The current Multispectral (MS) imaging systems used for facial recognition are based on multiple cameras having multiple sensors. These acquisition systems are normally slow because they take one MS image in several shots, which makes them unable to acquire images in real time and to capture moving scenes. On the other hand, currently there are snapshot multispectral imaging systems which integrate a single sensor with Multispectral Filter Arrays (MSFA) allow having at each acquisition an image on several spectra. These systems drastically reduce image acquisition time and are able to capture moving scenes in real time. This paper proposes a study of robust facial recognition using Multispectral Filter Array acquisition system. For this goal, a MSFA one-shot camera was used to collect the images and a robust facial recognition method based on Fast Discrete Curvelet Transform and Convolutional Neural Network is proposed. This camera covers the spectral range from 650 nm to 950 nm. A comparison of the facial recognition system using Multispectral Filter Arrays camera is made with those that using multiple cameras. Experimental results proved that face recognition systems whose acquisition systems are designed using MSFA perform more efficiently with an accuracy of 100%.

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Section: Related Workmentioning

confidence: 99%

Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

HOUSSOU¹,

Mahama²,

Gouton³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Visible sensors capture the reflected light from the surface of objects to create visible images [100][101][102][103], while infrared sensors obtain thermal images [104]. There are already many studies on the fusion of visible and thermal images [105][106][107][108][109][110][111][112][113][114][115][116][117][118] and such fusion has been integrated with face recognition [119][120][121]. Thermal images have some problems such as low contrast [122,123], blurred edge [124,125], temperature-sensitive [126][127][128], glass rejection [129][130][131][132], and little texture details [133,134].…”

Section: Introductionmentioning

confidence: 99%

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Zhang¹,

Wang²

2023

Computer Modeling in Engineering &Amp; Sciences

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Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR 3 C with BLS and IR 3 C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR 3 C with BLS, the recognition rates for the four challenging strategies are all 100%. In the model of IR 3 C with a triplet loss, the recognition rates are 94.61%, 94.61%, 96.95%, 96.23%, respectively. The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.

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“…Chen et al propose a neural network for creating fusion images for face recognition [ 55 ]. They state that the recognition improves when fusing data.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks

Gleichauf

Hennemann

Fahlbusch

et al. 2023

Sensors

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The monitoring of vital signs and increasing patient comfort are cornerstones of modern neonatal intensive care. Commonly used monitoring methods are based on skin contact which can cause irritations and discomfort in preterm neonates. Therefore, non-contact approaches are the subject of current research aiming to resolve this dichotomy. Robust neonatal face detection is essential for the reliable detection of heart rate, respiratory rate and body temperature. While solutions for adult face detection are established, the unique neonatal proportions require a tailored approach. Additionally, sufficient open-source data of neonates on the NICU is lacking. We set out to train neural networks with the thermal-RGB-fusion data of neonates. We propose a novel indirect fusion approach including the sensor fusion of a thermal and RGB camera based on a 3D time-of-flight (ToF) camera. Unlike other approaches, this method is tailored for close distances encountered in neonatal incubators. Two neural networks were used with the fusion data and compared to RGB and thermal networks. For the class “head” we reached average precision values of 0.9958 (RetinaNet) and 0.9455 (YOLOv3) for the fusion data. Compared with the literature, similar precision was achieved, but we are the first to train a neural network with fusion data of neonates. The advantage of this approach is in calculating the detection area directly from the fusion image for the RGB and thermal modality. This increases data efficiency by 66%. Our results will facilitate the future development of non-contact monitoring to further improve the standard of care for preterm neonates.

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A Novel Infrared and Visible Image Fusion Approach Based on Adversarial Neural Network

Cited by 4 publications

References 31 publications

Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks

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