Fully convolutional Deep Stacked Denoising Sparse Auto encoder network for partial face reconstruction
作者：
Highlights：
• In this partial face detection (PFD) is used to overcome the challenges involved in face detection and reconstruction.
• A novel PFD algorithm called Self- motivated feature mapping (SMFM) combining a FCN and DS-DSA algorithm.
• The proposed system focuses the feature maps from the FCN and used by the DS-DSA to perform partial face reconstruction.
• The spatial maps are generated by extracting the features from FCN and supplied as the input for partial reconstruction.
• By using principle component analysis and linear regression method and re-identification to the DS-DSA network.
摘要
•In this partial face detection (PFD) is used to overcome the challenges involved in face detection and reconstruction.•A novel PFD algorithm called Self- motivated feature mapping (SMFM) combining a FCN and DS-DSA algorithm.•The proposed system focuses the feature maps from the FCN and used by the DS-DSA to perform partial face reconstruction.•The spatial maps are generated by extracting the features from FCN and supplied as the input for partial reconstruction.•By using principle component analysis and linear regression method and re-identification to the DS-DSA network.
论文关键词：Partial face recognition,Deep learning algorithm,Fully convolutional network,Autoencoder
论文评审过程：Received 15 March 2021, Revised 26 April 2022, Accepted 7 May 2022, Available online 10 May 2022, Version of Record 19 May 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108783
