Semantic-based visual emotion recognition in videos-a transfer learning approach

Sekar, V.; Arunnehru, J.

doi:10.11591/ijece.v12i4.pp3674-3683

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The Euclidean distance between them is calculated as (16). After that, they are normalized by performing division operations through the length as (17).…”

Section: Visual Descriptormentioning

confidence: 99%

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Acoustic and visual geometry descriptor for multi-modal emotion recognition fromvideos

Ramyasree,

Sumanth Kumar

2024

IJEECS

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<span>Recognizing human emotions simultaneously from multiple data modalities (e.g., face, and speech) has drawn significant research interest, and numerous research contributions have been investigated in the affective computing community. However, most methods concentrate less on facial alignment and keyframe selection for audio-visual input. Hence, this paper proposed a new audio-visual descriptor, mainly concentrating on describing the emotion through only a few frames. For this purpose, we proposed a new self-similarity distance matrix (SSDM), which computes the spatial, and temporal distances through landmark points on the facial image. The audio signal is described through an asset of composite features, including statistical features, spectral features, formant frequencies, and energies. A support vector machine (SVM) algorithm is employed to classify both models, and the final results are fused to predict the emotion. Surrey audio-visual expressed emotion (SAVEE) and Ryerson multimedia research lab (RML) datasets are utilized for experimental validation, and the proposed method has shown significant improvement from the state of art methods.</span>

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“…The Euclidean distance between them is calculated as (16). After that, they are normalized by performing division operations through the length as (17).…”

Section: Visual Descriptormentioning

confidence: 99%

“…Using images from the internet will evaluate the models to discover the best model for recognizing human emotions and detecting faces [16]. The high-level motion feature frames are forwarded to the pre-trained CNN to distinguish the 17 emotions in the Geneva multimodal emotion portrayals dataset [17].…”

mentioning

confidence: 99%

Acoustic and visual geometry descriptor for multi-modal emotion recognition fromvideos

Ramyasree,

Sumanth Kumar

2024

IJEECS

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“…Another research [15] is covered that 10 am to 3:59 pm significantly reduces the chance of fatal injuries. Some researchers have used AI-based models for the early detection of intuition [16], [17]. Some studies have shown that heavier cars increase the chance of pedestrian fatalities [18].…”

Section: Related Workmentioning

confidence: 99%

Comparative analysis of influencing factors on pedestrian road accidents

Hafeez

Sheikh²,

Alshammari³

et al. 2023

Bulletin EEI

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Road accident data includes detailed information about incidents that occurred, such as where they happened, the severity of the accident, and the number of people on the road at the time. Such information is useful in determining the causes of accidents and developing potential countermeasures. This research aims to determine the factors that contribute to pedestrian fatalities and injuries in traffic accidents. This study examined 150 pedestrian-vehicle accidents that took place between 1990 and 2021 in forty countries. Eleven factors have been identified as the major causes of accidents. The categorical principal component analysis (CATPCA) technique is used to reduce the number of dimensions and identify the elements that contribute to accidents. The eleven variables are classified into three groups: human factors, roadway environment, and vehicle attributes. The study found that car speed, weather, lighting, traffic conditions, area types, accident locations, and road conditions all had a significant impact on pedestrian accidents and fatalities. The findings show that a pedestrian's state (walking, running) and intention significantly increase the risk of serious injuries and death. The analysis of the driver's status suggests that the driver's intentions may also play a role in car accidents.

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“…It enhances humancomputer interaction, aids mental health monitoring, and improves customer service by gauging emotions [16], [17]. Additionally, it offers valuable insights in fields like market research and advertising, making it a valuable tool for understanding and responding to human emotions [18]. However, while recognizing a face, there are certain challenges such as refined expressions, ambiguous emotions, individual differences, and context dependency.…”

Section: Introductionmentioning

confidence: 99%

Facial emotion recognition using enhanced multi-verse optimizer method

Gummula,

Arumugam,

Aranganathan

2024

IJECE

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In recent years, facial emotion recognition has gained significant improvement and attention. This technology utilizes advanced algorithms to analyze facial expressions, enabling computers to detect and interpret human emotions accurately. Its applications span over a wide range of fields, from improving customer service through sentiment analysis, to enhancing mental health support by monitoring emotional states. However, there are several challenges in facial emotion recognition, including variability in individual expressions, cultural differences in emotion display, and privacy concerns related to data collection and usage. Lighting conditions, occlusions, and the need for diverse datasets also impacts accuracy. To solve these issues, an enhanced multi-verse optimizer (EMVO) technique is proposed to improve the efficiency of recognizing emotions. Moreover, EMVO is used to improve the convergence speed, exploration-exploitation balance, solution quality, and the applicability in different types of optimization problems. Two datasets were used to collect the data, namely YouTube and surrey audio-visual expressed emotion (SAVEE) datasets. Then, the classification is done using the convolutional neural networks (CNN) to improve the performance of emotion recognition. When compared to the existing methods shuffled frog leaping algorithm-incremental wrapper-based subset selection (SFLA-IWSS), hierarchical deep neural network (H-DNN) and unique preference learning (UPL), the proposed method achieved better accuracies, measured at 98.65% and 98.76% on the YouTube and SAVEE datasets, respectively.

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Semantic-based visual emotion recognition in videos-a transfer learning approach

Cited by 5 publications

References 17 publications

Acoustic and visual geometry descriptor for multi-modal emotion recognition fromvideos

Acoustic and visual geometry descriptor for multi-modal emotion recognition fromvideos

Comparative analysis of influencing factors on pedestrian road accidents

Facial emotion recognition using enhanced multi-verse optimizer method

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