Emotion recognition from thermal infrared images using deep Boltzmann machine

Wang, Shangfei; He, Menghua; Gao, Zhen; He, Shan; Ji, Qiang

doi:10.1007/s11704-014-3295-3

Cited by 40 publications

(27 citation statements)

References 16 publications

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“…LGBP-TOP [107], Riemannian manifolds [108], TDHF [109], StaFs [104], [109] Local Static: Mean intensity [110], Eigenimages [75], GLCM [104], [111] Dynamic: BoW Hist. [112] Geometry Global Static: Landmark locations [113], Landmark distances [114], PBVD [115], Candide Facial Grid [116], Geometric distance [117], [118], EDM [92], 3D mesh+Manifolds [119], Depth map [120], [121], LBP [122], [123], Curvature maps [124], [125] Dynamic: Optical flow [126], MHI [127], FFD [127], Level curve deformations [128], FFD+QT [129], LBP-TOP [130] Local Static: Curvature labels [131], Closed curves [132], DMCIC+HOG [125], Depth map+SIFT [120], BFSC [133] Dynamic: MU [134], [135], FAP [136], [137], EDM+Motion vectors [138] Appearance + Geometry Static: Shape+Color [139], Landmark distances+Angles+HOG [140], 3DMM [141], SFAM+LBP [142],…”

Section: Recognition Of Fesmentioning

confidence: 99%

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Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

Corneanu

Simón

Cohn

et al. 2016

IEEE Trans. Pattern Anal. Mach. Intell.

452

202

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Abstract-Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.

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Section: Recognition Of Fesmentioning

confidence: 99%

“…As such, these are also applicable to the RGB case. In [104] a combination of StaFs, 2D-DCT and GLCM features is used, extracting both local and global information.…”

Section: Feature Extractionmentioning

confidence: 99%

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

Corneanu

Simón

Cohn

et al. 2016

IEEE Trans. Pattern Anal. Mach. Intell.

452

202

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“…Here we are taking the tomato and wheat crops which are purely organic materials and with complete nutrition diet as per Chinese standards used for planting, cropping and maintaining. Here for developing of our project [7] virus was provided by the institute of agricultural sciences and that virus was injected to these crops later these crops were separated from the overall crop and separate them into a fertilized area because not to spread the germs/virus to the healthy plants.…”

Section: A Collection Of Plants and Bacteria Separatelymentioning

confidence: 99%

Utilization of Infrared Warm Imaging for the Fast Analysis of Yield Illness

Kumar*,

Kumar,

Agrawal

2019

IJEAT

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Plants at the very small age suffer from different types of diseases in different seasons. For the better growing of plants needs a special care and attention and need necessary requirement such as water, sunlight and also some pesticides from protecting plants without affecting by any type of insects. Presently there are various testing methods for identifying and safeguarding the plants from different germs. Here we have taken tomato and wheat plants as an example for identifying mechanism. In these two plants we observed that the temperature changes effects as the change in the plant growing method and also observed that rainy season was mostly affected one compared to all for viral infections. For our experiments we have taken the time period for tomato and wheat for taking the disease affected percentage was 5 and 7 days respectively. Our experiments proved that by using the method of Infrared Thermal Imaging and MTD(Modern Tool and Die making Company) mechanisms we can identify the health and condition of the plants by making accurate application and spraying..

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“…emerged as an automatic feature learning tool without manual intervention. While they have achieved reasonably good performance in different applications such as digital recognition and human detection [7][8] [9][10] [11] [12], the depth network will learn the unfavorable feature description when regards the original pixels as input, since these pixels are affected by changing lighting, posture, and so on.. Moreover, in the real situation, the training sample set is usually small and not sufficient to adjust the network weights, the problem also weakens the performance of the network.…”

Section: Introductionmentioning

confidence: 99%

“…[7]used Convolution Neural Network (CNN) with noisy labels, [8]developed Cascaded Deep Auto-Encoder Network (CDAN)approach, [9][10]adopted Deep Belief Networks(DBN), [11][12]used Deep Boltzmann Machine(DBM), [13]built multiple deep ConvNets to learn high-level global features of face and trained RBM for classification. Although the above methods of extracting features are joint and automatic, the extracted features are sensitive to illumination, posture, and other interference in unconstrained environments.…”

Section: Introductionmentioning

confidence: 99%

A Combined GLQP and DBN-DRF for Face Recognition in Unconstrained Environments

Hu¹,

Yang²

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-This paper proposes a novel approach for accurate and robust face recognition by using Local Quantized Patterns computed from gabor-filtered images(GLQP) and Deep Belief Network ensembled dynamic random forests(DBN-DRF). GLQP is a kind of local pattern feature extractor based on gabor filters applying, it makes use of vector quantization and lookup table to let local features become more expressive without sacrificing simplicity and computational efficiency. DBN-DRF is a new deep architecture we proposed, in which dynamic random forests classifier is employed to replace inherent Softmax classifier or SVM to achieve a decent classification result at the top of network. GLQP exploits low-level local features that are used as input to DBN-DRF, which further extracts high-level abstract features for classification. Our architecture is trained and evaluated on two challenging face recognition datasets(FERET and LFW), the experiments result show our approach is competitive or better than the state of the arts.

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Emotion recognition from thermal infrared images using deep Boltzmann machine

Cited by 40 publications

References 16 publications

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

Utilization of Infrared Warm Imaging for the Fast Analysis of Yield Illness

A Combined GLQP and DBN-DRF for Face Recognition in Unconstrained Environments

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