人工智能
计算机科学
卷积神经网络
模式识别(心理学)
面子(社会学概念)
深度学习
特征(语言学)
降噪
面部识别系统
生成对抗网络
迭代重建
计算机视觉
社会科学
社会学
语言学
哲学
作者
Savithramma P. Dinesh‐Kumar,M. Manikandan
标识
DOI:10.1016/j.patcog.2022.108783
摘要
Face recognition is one of the most successful applications of image analysis. Since 1960s, automatic face recognition research has been carried out, but the problem is still unresolved. Therefore, in this manuscript, a novel Partial face reconstruction (PFR) algorithm called Self- motivated feature mapping (SMFM) combining a Fully Convolutional Network (FCN) and Deep Stacked Denoising Sparse Autoencoders (DS-DSA) algorithm is proposed to overcome the challenges. The proposed approach focuses on the generation of feature maps from the Fully Convolutional Network and it is used Deep Stacked Denoising Sparse Autoencoders to perform the partial face reconstruction. The spatial maps are generated by extracting the features from Fully Convolutional Network and it is supplied as the input for partial reconstruction and re-identification to the Deep Stacked Denoising Sparse Autoencoders network. The main aim of the proposed work is "to enhance the accuracy during facial reconstruction". The proposed approach is implemented in MATLAB platform. The performance of the proposed approach attains 23.45% and 20.41% accuracy,25.93`% and 19.43% sensitivity, 22.21% and 24.41% precision and20.21% and 23.41% Specificity greater than the existing approaches, like Partial Face Reconstruction using generative adversarial networks (GANs), Partial Face Reconstruction using Deep Recurrent neural network (DRNN).
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