Multi-Class Confidence Detection Using Deep Learning Approach Article Swipe
Amna Mujahid
,
Muhammad Aslam
,
Muhammad Usman Ghani Khan
,
A. M. Martínez-Enríquez
,
Nazeef Ul Haq
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13095567
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13095567
The advancement of both the fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has enabled the development of effective automatic systems for analyzing human behavior. It is possible to recognize gestures, which are frequently used by people to communicate information non-verbally, by studying hand movements. So, the main contribution of this research is the collected dataset, which is taken from open-source videos of the relevant subjects that contain actions that depict confidence levels. The dataset contains high-quality frames with minimal bias and less noise. Secondly, we have chosen the domain of confidence determination during social issues such as interviews, discussions, or criminal investigations. Thirdly, the proposed model is a combination of two high-performing models, i.e., CNN (GoogLeNet) and LSTM. GoogLeNet is the state-of-the-art architecture for hand detection and gesture recognition. LSTM prevents the loss of information by keeping temporal data. So the combination of these two outperformed during the training and testing process. This study presents a method to recognize different categories of Self-Efficacy by performing multi-class classification based on the current situation of hand movements using visual data processing and feature extraction. The proposed architecture pre-processes the sequence of images collected from different scenarios, including humans, and their quality frames are extracted. These frames are then processed to extract and analyze the features regarding their body joints and hand position and classify them into four different classes related to efficacy, i.e., confidence, cooperation, confusion, and uncomfortable. The features are extracted using a combination framework of customized Convolutional Neural Network (CNN) layers with Long Short-Term Memory (LSTM) for feature extraction and classification. Remarkable results have been achieved from this study representing 90.48% accuracy with effective recognition of human body gestures through deep learning approaches.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13095567
- https://www.mdpi.com/2076-3417/13/9/5567/pdf?version=1683624428
- OA Status
- gold
- Cited By
- 5
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367627735
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367627735Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app13095567Digital Object Identifier
- Title
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Multi-Class Confidence Detection Using Deep Learning ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-30Full publication date if available
- Authors
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Amna Mujahid, Muhammad Aslam, Muhammad Usman Ghani Khan, A. M. Martínez-Enríquez, Nazeef Ul HaqList of authors in order
- Landing page
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https://doi.org/10.3390/app13095567Publisher landing page
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https://www.mdpi.com/2076-3417/13/9/5567/pdf?version=1683624428Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/13/9/5567/pdf?version=1683624428Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Gesture, Confusion matrix, Feature extraction, Class (philosophy), Process (computing), Deep learning, Machine learning, Pattern recognition (psychology), Feature (linguistics), Philosophy, Linguistics, Operating systemTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 2, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
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50Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2023-04-30 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3136057722, https://openalex.org/W3134115326, https://openalex.org/W3022965290, https://openalex.org/W1498883201, https://openalex.org/W4281568103, https://openalex.org/W3138674438, https://openalex.org/W3088004292, https://openalex.org/W2903018492, https://openalex.org/W2990283237, https://openalex.org/W6751404144, https://openalex.org/W2918626955, https://openalex.org/W2904559914, https://openalex.org/W2540981342, https://openalex.org/W3044285175, https://openalex.org/W2583633543, https://openalex.org/W2973305447, https://openalex.org/W2533503513, https://openalex.org/W1983705368, https://openalex.org/W2108333036, https://openalex.org/W2946490545, https://openalex.org/W3118735866, https://openalex.org/W4285130333, https://openalex.org/W1567302070, https://openalex.org/W2972235385, https://openalex.org/W3160469966, https://openalex.org/W3042899078, https://openalex.org/W2013760576, https://openalex.org/W2964350391, https://openalex.org/W3090453110, https://openalex.org/W3173590174, https://openalex.org/W3215807065, https://openalex.org/W2610635404, https://openalex.org/W2803233114, https://openalex.org/W2895488603, https://openalex.org/W2028292455, https://openalex.org/W3000698475, https://openalex.org/W2794679204, https://openalex.org/W2604566795, https://openalex.org/W2081934410, https://openalex.org/W2952745542, https://openalex.org/W2963601560, https://openalex.org/W2963859396, https://openalex.org/W2964093990, https://openalex.org/W3048921023, https://openalex.org/W2894835560, https://openalex.org/W2995932540, https://openalex.org/W2955948662, https://openalex.org/W2770887258, https://openalex.org/W2780179792, https://openalex.org/W2801271919 |
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| abstract_inverted_index.we | 87 |
| abstract_inverted_index.CNN | 117 |
| abstract_inverted_index.So, | 47 |
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| abstract_inverted_index.deep | 283 |
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