Toward jointly understanding social relationships and characters from videos

Teng, Yiyang; Song, Chenguang; Wu, Bin

doi:10.1007/s10489-021-02738-z

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…CNN structures generally include an input layer, convolution layer, down-sampling layer, and output layer. The convolution layer and down-sampling layer appear alternately between the input layer and the output layer and are important modules for feature extraction [19]. At the same time, activation functions are also an important part of CNN, which can increase the expression ability and learning ability of models.…”

Section: Research On Face Recognition and Cnn Optimizationmentioning

confidence: 99%

Application of CNN based Regognition Technology in Public English Teaching in College Intelligent Classroom

2024

SCPE

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With the advancement of intelligent technology, CNN-based recognition technology has been integrated into public English teaching in universities. This implementation contributes to enhancing teaching quality, nurturing students’ English proficiency, and holds significant educational and practical value. To address the issue of low traditional attendance efficiency in intelligent classrooms for public English teaching, a face recognition model based on CNN recognition technology has been developed. R-CNN is utilized for object detection, along with pyramid pooling and non-maximum suppression to acquire the optimal candidate region for face detection. Furthermore, K-Means clustering is combined to enhance Fast R-CNN, thereby improving detection accuracy. Experimental results demonstrated that among the three networks - Fast R-CNN, Faster R-CNN, and CNN -Faster R-CNN maintained a high recognition rate and exhibited faster convergence speed, showcasing superior overall performance. Specifically, at 500 iterations, the three networks require 23.7 seconds, 26.8 seconds, and 34.2 seconds, respectively. For facial expression recognition, Faster R-CNN achieved the highest recognition rate, indicating its exceptional detection efficiency and potential for aiding teaching management. This study offers novel technical support for public English teaching in intelligent university classrooms, effectively enhancing teaching efficacy and learning experiences. Its practical significance extends to promoting educational reform and improvement.

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Section: Research On Face Recognition and Cnn Optimizationmentioning

confidence: 99%

Application of CNN based Regognition Technology in Public English Teaching in College Intelligent Classroom

2024

SCPE

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“…Another instance occurs in important video meetings, such that if we can divide the encryption meeting video into a multilevel approach with different levels that contain different amounts of information, the leader can set multiple grades of the users according to the amounts of visual information in the video. Only in this way will the meeting video be even more effective in people's work and lives [9,10].…”

Section: Introductionmentioning

confidence: 99%

A multi-level approach with visual information for encrypted H.265/HEVC videos（China MM）

Wen

Zhang

et al. 2022

Preprint

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High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption. To achieve high video security, to the best of our knowledge, almost all of the existing HEVC encryption algorithms mainly encrypt the whole video, such that the user without permissions cannot obtain any viewable information. However, these encryption algorithms cannot meet the needs of customers who need part of the information but not the full information in the video. In many cases, such as professional paid videos or video meetings, users would like to observe some visible information in the encrypted video of the original video to satisfy their requirements in daily life. Aiming at this demand, this paper proposes a multi-level encryption scheme that is composed of lightweight encryption, medium encryption and heavyweight encryption, where each encryption level can obtain a different amount of visual information. First, we employ AES-CTR to generate a pseudo-random number sequence. Then, the main syntax elements in the H.265/HEVC encoding process are encrypted by a pseudorandom sequence. In the lightweight encryption level, the syntax element of the luma intraprediction model is chosen for encryption. In the medium encryption level, the syntax element of the discrete cosine transform (DCT) coefficient sign is employed for scrambling encryption. In the heavyweight encryption level, syntax elements of both the luma intraprediction model and the DCT coefficient sign are encrypted simultaneously by the pseudorandom sequence. It is found that both encrypting the luma intraprediction model (IPM) and scrambling the syntax element of the DCT coefficient sign can achieve the performance of a distorted video in which there is still residual visual information, while encrypting both of them can implement the intensity of encryption and one cannot gain any visual information. The experimental results meet our expectations appropriately, indicating that there is a different amount of visual information in each encryption level. Meanwhile, users can flexibly choose the encryption level according to their various requirements.

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