Human Emotions Recognition from Thermal Images using Yolo Algorithm

Chaitanya,; Sarath, S; Malavika,; Prasanna,; Karthik,

doi:10.1109/iccsp48568.2020.9182148

Cited by 15 publications

(6 citation statements)

References 4 publications

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“…Many detectors were used for recognizing emotion and for detecting faces from images or videos. Sarah et al [ 12 ] used YOLO in their study for detecting faces from images. YOLO has many versions and is mostly used for the object detection task, and a face is also an object, which is it was used for emotion detection tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM

Haider

Yang

Lee

et al. 2023

Sensors

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Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions increases the diversity and complexity of classification problems. In this paper, we have proposed a novel and intelligent approach for the classification of human facial emotions. The proposed approach comprises customized ResNet18 by employing transfer learning with the integration of triplet loss function (TLF), followed by SVM classification model. Using deep features from a customized ResNet18 trained with triplet loss, the proposed pipeline consists of a face detector used to locate and refine the face bounding box and a classifier to identify the facial expression class of discovered faces. RetinaFace is used to extract the identified face areas from the source image, and a ResNet18 model is trained on cropped face images with triplet loss to retrieve those features. An SVM classifier is used to categorize the facial expression based on the acquired deep characteristics. In this paper, we have proposed a method that can achieve better performance than state-of-the-art (SoTA) methods on JAFFE and MMI datasets. The technique is based on the triplet loss function to generate deep input image features. The proposed method performed well on the JAFFE and MMI datasets with an accuracy of 98.44% and 99.02%, respectively, on seven emotions; meanwhile, the performance of the method needs to be fine-tuned for the FER2013 and AFFECTNET datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM

Haider

Yang

Lee

et al. 2023

Sensors

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“…In the past decades, many researchers have investigated subjects’ reactions and potentials of different emotion recognition techniques, including speech, non-verbal audition, facial expression, visual and thermal images, peripheral neural signals, and central neural system signals [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ].…”

Section: Related Workmentioning

confidence: 99%

“…Infrared thermography (IRT) captures the radiation of energy from the use of sensors. Investigations revealed that there is a connection between emotions and blood flow, and such a connection can be recognized using infrared thermography [ 32 , 33 , 34 ].…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Wosiak

Dura

2020

Sensors

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Based on the growing interest in encephalography to enhance human–computer interaction (HCI) and develop brain–computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band—electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions—valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20–8.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods.

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“…Faster R-CNN is used to represent deep learning object detection algorithms. 2) Regression-based deep learning object detection algorithms are represented as you only look once (YOLO) (Chaitanya et al, 2020). The former predicts the speeds of between 7 and 18 frames per second, which is too time-consuming.…”

Section: Introductionmentioning

confidence: 99%

YOLOv4-Lite–Based Urban Plantation Tree Detection and Positioning With High-Resolution Remote Sensing Imagery

Zheng

2022

Front. Environ. Sci.

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Automatic tree identification and position using high-resolution remote sensing images are critical for ecological garden planning, management, and large-scale environmental quality detection. However, existing single-tree detection methods have a high rate of misdetection in forests not only due to the similarity of background and crown colors but also because light and shadow caused abnormal crown shapes, resulting in a high rate of misdetections and missed detection. This article uses urban plantations as the primary research sample. In conjunction with the most recent deep learning method for object detection, a single-tree detection method based on the lite fourth edition of you only look once (YOLOv4-Lite) was proposed. YOLOv4’s object detection framework has been simplified, and the MobileNetv3 convolutional neural network is used as the primary feature extractor to reduce the number of parameters. Data enhancement is performed for categories with fewer single-tree samples, and the loss function is optimized using focal loss. The YOLOv4-Lite method is used to detect single trees on campus, in an orchard, and an economic plantation. Not only is the YOLOv4-Lite method compared to traditional methods such as the local maximum value method and the watershed method, where it outperforms them by nearly 46.1%, but also to novel methods such as the Chan-Vese model and the template matching method, where it outperforms them by nearly 26.4%. The experimental results for single-tree detection demonstrate that the YOLOv4-Lite method improves accuracy and robustness by nearly 36.2%. Our work establishes a reference for the application of YOLOv4-Lite in additional agricultural and plantation products.

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Human Emotions Recognition from Thermal Images using Yolo Algorithm

Cited by 15 publications

References 4 publications

Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM

Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM

Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

YOLOv4-Lite–Based Urban Plantation Tree Detection and Positioning With High-Resolution Remote Sensing Imagery

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