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4 May 2022 FTCNet: a lightweight model for large-pose face alignment
Shang Shi Jr., Fei Long Sr.
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Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 121720W (2022) https://doi.org/10.1117/12.2634424
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China
Abstract
Face alignment and face reconstruction are hotspots in computer vision and artificial intelligence. Aiming at the problem of large model and low accuracy of large pose face alignment algorithm, a new lightweight network model FTCNet is designed and implemented. First, deep separable convolution is used to build a lightweight deep neural network model, which directly inputs images in an end-to-end manner for face alignment. Secondly, the network model classifies and predicts pose parameters, shape parameters and expression parameters, and improves the performance of the model through the feature transfer mechanism. Tested on multiple datasets, the experimental results show that the FTCNet gets small, fast, and high-quality face alignment and reconstruction results for unconstrained face images.
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Shang Shi Jr. and Fei Long Sr. "FTCNet: a lightweight model for large-pose face alignment", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 121720W (4 May 2022); https://doi.org/10.1117/12.2634424
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KEYWORDS
Convolution
3D modeling
3D image processing
Machine vision
Reconstruction algorithms
Neural networks
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