Profile Modeling in Hierarchical Deep Architecture by Mutual Support
type: CreativeWork
description: Despite significant advances in the field of face analysis over last decade, the current studies are still limited to specific face computation tasks using deep learning approaches. In this paper, we propose an end-to-end hierarchical deep learning structure, called Multi-Features Convolutional Neural Networks (MFCNN), which can comprehensively implement face analysis including age, gender, race and emotion. Moreover, we take the advantages of the mutual support among different facial features from individual tasks to improve the performance of our model. We also contribute one all-labeling dataset called Multiple Facial Features Computation (MFFC) based on Apparent-age-V2 dataset. Firstly, we train four different VGG-based classifiers to analyze facial features independently like age, gender, etc. using MFFC. Then, we systematically introduced cross-task verification approaches to demonstrate features extracted from different pre-trained models have mutual support to each other. After that, multi-task features extracted from pre-trained models are integrated to train MFCNN. Furthermore, feature fusion strategies also have been implemented to enhance our framework from accuracy and time complexity. Finally, the experimental results demonstrate that MFCNN outperforms state-of-the-art methods of face analysis by 10.3% in average and the best result improves up to 21.3% margin for emotion estimation.
abstract: Despite significant advances in the field of face analysis over last decade, the current studies are still limited to specific face computation tasks using deep learning approaches. In this paper, we propose an end-to-end hierarchical deep learning structure, called Multi-Features Convolutional Neural Networks (MFCNN), which can comprehensively implement face analysis including age, gender, race and emotion. Moreover, we take the advantages of the mutual support among different facial features from individual tasks to improve the performance of our model. We also contribute one all-labeling dataset called Multiple Facial Features Computation (MFFC) based on Apparent-age-V2 dataset. Firstly, we train four different VGG-based classifiers to analyze facial features independently like age, gender, etc. using MFFC. Then, we systematically introduced cross-task verification approaches to demonstrate features extracted from different pre-trained models have mutual support to each other. After that, multi-task features extracted from pre-trained models are integrated to train MFCNN. Furthermore, feature fusion strategies also have been implemented to enhance our framework from accuracy and time complexity. Finally, the experimental results demonstrate that MFCNN outperforms state-of-the-art methods of face analysis by 10.3% in average and the best result improves up to 21.3% margin for emotion estimation.
author: Peng, Honglai
author: Han, Meng
author: He, Jing (Selena)
keywords: Face computation, CNNs, Deep Learning, Features Fusion, Transfer Learning., Computer Sciences, Data Science, Other Computer Sciences
date published: 2021-05-04T07:00:00Z
date modified: 2026-07-24T02:43:51Z
provider: Kennesaw State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Kennesaw State University