Mr. Shulin Lv Profile

Shulin Lv

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Proceedings Article | 2 March 2022 Paper

Research on real-time facial landmark detection model that can deal with occlusion

Shulin Lv, Shang Wu, Jie Liu, Chunye Gong

Proceedings Volume 12158, 1215816 (2022) https://doi.org/10.1117/12.2626973

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With the wide use of face tasks on mobile terminals, facial landmark detection faces new challenges of real-time and occlusion. Therefore, this paper designs a real-time facial landmark detection model that can deal with occlusion. The model of this paper includes the backbone network and the auxiliary network. The function of the backbone network is to locate facial landmarks. The backbone network uses the lightweight network module ShufflenetV2 to ensure the realtime reasoning of the model, and uses dynamic convolution and Wing loss function to improve the positioning accuracy of the model. The function of the auxiliary network is to predict landmarks’ visibility. The auxiliary network helps the backbone network focusing attention on visible landmarks, so as to deal with occlusion. The experimental results show that the size of the model is 1.3MB, the reasoning speed of the model on Intel i5-8250u CPU is 64.63 pictures per second, and the prediction accuracy on the WFLW dataset of the model is 85.72%. The model performs better than PFLD in size, speed, and accuracy.

Proceedings Article | 22 December 2021 Paper

Feature extraction algorithm for payload based on tree structure representation

Shang Wu, Dongsheng Xu, Shulin Lv

Proceedings Volume 12156, 121560D (2021) https://doi.org/10.1117/12.2626489

KEYWORDS: Feature extraction, Data modeling, Performance modeling, Network security, Model-based design, Machine learning

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The payload in the network traffic contains a variety of information related to the traffic. Identifying anomalous attack behaviors through the payload is a crucial method to protect against network attacks effectively. The payload structure is complex, which contains a large number of contents related to the security field, and these contents have contextual semantics strong relevance. To fully express the relevance of payload contents and better improve the quality of payload feature extraction, this paper proposes a feature extraction algorithm for payload based on tree structure representation, called TSR. The experimental results show that, compared with the existing feature extraction algorithms, the ROC-AUC of TSR increases by 3.32% on average, and the PR-AUC increases by 24.15% on average.

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