Optimal fusion aided face recognition from visible and thermal face images

Kanmani, Madheswari; Venkateswaran, N.

doi:10.1007/s11042-020-08628-9

Cited by 28 publications

(9 citation statements)

References 46 publications

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“…Through the use of multispectral images in facial recognition, it is possible to overcome some characteristic gaps in the spectral bands. As is the case with LWIR spectral band that, because they are not influenced by differences in luminosity, are able to complement the VIS images that are, as several authors had stated in their works [39], [43], [49].…”

Section: Discussionmentioning

confidence: 97%

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Multispectral Facial Recognition: A Review

2020

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Multispectral images are images with more than one channel acquired in different bands or spectral ranges of the electromagnetic spectrum. Each one has specific details that can be exploited in facial recognition applications. In particular, to detect facial expression variations, pose variations and presentation attacks, a facial analysis system can benefit not only of images from the visible spectral band but also of infrared images. In this paper we perform a review of the state of the art methods used in multispectral facial recognition using images from the visible spectral band and also from the Near Infrared, Short Wavelength Infrared and Long Wavelength Infrared sub-bands. The public multispectral databases for facial analysis are identified, and a comparison is made, taking into consideration their specifications. The multispectral facial recognition methods are classified according to their basic working principle, from the traditional Fusion and Subspace methods to the more recent Deep Neural Networks.INDEX TERMS Face recognition, multispectral image, infrared image. V. METHODS

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Section: Discussionmentioning

confidence: 97%

“…Kanmani and Narasimhan [39] proposed three optimization based fusion methods that aid the heterogeneous face recognition problem. In the first and second methods, the input image was decomposed into high and low frequency coefficients through dual tree discrete wavelet transform.…”

Section: Fusionmentioning

confidence: 99%

Multispectral Facial Recognition: A Review

2020

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“…Kanmani and Narasimhan [ 15 ] suggested an Eigen face recognition system that benefits from the integration of visual and thermal facial pictures to increase the accuracy of face identification. The dual-tree discrete wavelet transform (DT-DWT) is the domain in which the first two fusion methods operate, whereas the CT is the domain in which the third method operates.…”

Section: Literature Surveymentioning

confidence: 99%

Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

Singothu

Chandana²

2022

Sensors

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Thermal cameras, as opposed to RBG cameras, work effectively in extremely low illumination situations and can record data outside of the human visual spectrum. For surveillance and security applications, thermal images have several benefits. However, due to the little visual information in thermal images and intrinsic similarity of facial heat maps, completing face identification tasks in the thermal realm is particularly difficult. It can be difficult to attempt identification across modalities, such as when trying to identify a face in thermal images using the ground truth database for the matching visible light domain or vice versa. We proposed a method for detecting objects and actions on thermal human face images, based on the classification of five different features (hat, glasses, rotation, normal, and hat with glasses) in this paper. This model is presented in five steps. To improve the results of feature extraction during the pre-processing step, initially, we resize the images and then convert them to grayscale level using a median filter. In addition, features are extracted from pre-processed images using principle component analysis (PCA). Furthermore, the horse herd optimization algorithm (HOA) is employed for feature selection. Then, to detect the human face in thermal images, the LeNet-5 method is used. It is utilized to detect objects and actions in face areas. Finally, we classify the objects and actions on faces using the ANU-Net approach with the Monarch butterfly optimization (MBO) algorithm to achieve higher classification accuracy. According to experiments using the Terravic Facial Infrared Database, the proposed method outperforms “state-of-the-art” methods for face recognition in thermal images. Additionally, the results for several facial recognition tasks demonstrate good precision.

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“…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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Optimal fusion aided face recognition from visible and thermal face images

Cited by 28 publications

References 46 publications

Multispectral Facial Recognition: A Review

Multispectral Facial Recognition: A Review

Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

Broad Learning System for Tackling Emerging Challenges in Face Recognition

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