Paper Info
Title
An Improved Selective Facial Extraction Model for Age Estimation
Abstract
In this paper, we propose an improved end-to-end learning algorithm to address the aggregation of multiclass classification and regression for age estimation by using deep Convolutional Neural Networks (CNNs). Inspired by Soft Stagewise Regression Network (SSR-Net), we take residual units embedded with channel and spatial feature response correlation values and dynamically adopted the kernel corresponding to these feature maps into our model. In addition, we used weight normalization at each layer by using input samples at the beginning of training and this weight normalization was beneficial both in terms of accuracy as well as training time. We validate the proposed model based on benchmark datasets and compare the MAE with seven other mainstream networks. The results reveal that our model achieves an improved performance. Our contribution is an updated algorithm for age estimation by adopting the latest attention and normalization mechanisms for balancing the efficiency and accuracy of the proposed model.
Year
DOI
Venue
2019
10.1109/IVCNZ48456.2019.8960965
2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Keywords
Field
DocType
age estimation,end-to-end learning algorithm,multiclass classification,deep convolutional neural networks,SSR-Net,residual units,spatial feature response correlation values,feature maps,weight normalization,selective facial extraction model,CNNs,soft stagewise regression network
Kernel (linear algebra),Residual,Normalization (statistics),Pattern recognition,Regression,Convolutional neural network,Computer science,Communication channel,Correlation,Artificial intelligence,Multiclass classification
Conference
ISSN
ISBN
Citations 
2151-2191
978-1-7281-4188-6
1
PageRank 
References 
Authors
0.36
0
4
Name
Order
Citations
PageRank
Chengwen Song110.36
Lingmin He210.36
Wei Qi Yan310.36
Parma Nand410.36