Face Detection and Recognition Using Face Mesh and Deep Neural Network

Hangaragi, Shivalila; Singh, Tripty; Neelima, N.

doi:10.1016/j.procs.2023.01.054

Cited by 65 publications

(12 citation statements)

References 2 publications

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“…However, the results of Table 3 are very far from the predictions made for this combination, and the combination of the proposed descriptor with the support vector machine classifier could not obtain a favorable detection rate in the face of image damage [42,43]. These results show that although facial expression recognition in the unchallenged mode has a very high recognition rate, in the condition that the image pixel values are destroyed under conditions such as noise, the recognition rate decreases significantly, so the use of a support vector machine classifier can not obtain good results.…”

Section: Lab Resultsmentioning

confidence: 75%

Improving recognition accuracy for facial expressions using scattering wavelet

Davari,

Harooni,

Nasr

et al. 2024

EAI Endorsed Trans AI Robotics

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One of the most evident and meaningful feedback about people’s emotions is through facial expressions. Facial expression recognition is helpful in social networks, marketing, and intelligent education systems. The use of Deep Learning based methods in facial expression identification is widespread, but challenges such as computational complexity and low recognition rate plague these methods. Scatter Wavelet is a type of Deep Learning that extracts features from Gabor filters in a structure similar to convolutional neural networks. This paper presents a new facial expression recognition method based on wavelet scattering that identifies six states: anger, disgust, fear, happiness, sadness, and surprise. The proposed method is simulated using the JAFFE and CK+ databases. The recognition rate of the proposed method is 99.7%, which indicates the superiority of the proposed method in recognizing facial expressions.

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Section: Lab Resultsmentioning

confidence: 75%

Improving recognition accuracy for facial expressions using scattering wavelet

Davari,

Harooni,

Nasr

et al. 2024

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

show abstract

“…The integration of force sensors into the robotic arms can minimize discomfort by avoiding over irritation of the nasal cavity [ 7 ]. Moreover, implementing a system that can scan facial structures and rapidly tailor the depth and route could reduce the time that patients spend in the sampling room, thereby alleviating stress [ 22 ]. Further multidisciplinary cooperation is required.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the safety, effectiveness, and usability of swab robot vs. manual nasopharyngeal specimen collection

Yu,

Hsieh,

Chen

et al. 2023

Heliyon

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“…The issue of detecting and identifying faces in broadcast videos is a well-researched subject, and a comprehensive review of the extensive literature on face detection and recognition has been presented in [16][17]. Many face recognition techniques are tested on controlled environments and for a limited number of faces and poses, such as in serials or movies.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

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In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display player personal data. The system uses AdaBoost to detect player and player face and use PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players' faces even with several conditions such as occlusion, non-uniform illumination and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system's methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

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Face Detection and Recognition Using Face Mesh and Deep Neural Network

Cited by 65 publications

References 2 publications

Improving recognition accuracy for facial expressions using scattering wavelet

Improving recognition accuracy for facial expressions using scattering wavelet

Comparison of the safety, effectiveness, and usability of swab robot vs. manual nasopharyngeal specimen collection

Automatic Player Face Detection and Recognition for Players in Cricket Games

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