scholarly journals Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation

2018 ◽  
Vol 8 (9) ◽  
pp. 1601
Author(s):  
Chaoqun Hong ◽  
Zhiqiang Zeng ◽  
Xiaodong Wang ◽  
Weiwei Zhuang
Keyword(s):  
Neural Networks ◽  
Age Estimation ◽  
Feature Fusion ◽  
Estimation Method ◽  
Feature Space ◽  
Image Features ◽  
Low Rank ◽  
Face Images ◽  
Low Rank Representation ◽  
Multiple Network

Image-based age estimation is a challenging task since there are ambiguities between the apparent age of face images and the actual ages of people. Therefore, data-driven methods are popular. To improve data utilization and estimation performance, we propose an image-based age estimation method. Theoretically speaking, the key idea of the proposed method is to integrate multi-modal features of face images. In order to achieve it, we propose a multi-modal learning framework, which is called Multiple Network Fusion with Low-Rank Representation (MNF-LRR). In this process, different deep neural network (DNN) structures, such as autoencoders, Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and so on, can be used to extract semantic information of facial images. The outputs of these neural networks are then represented in a low-rank feature space. In this way, feature fusion is obtained in this space, and robust multi-modal image features can be computed. An experimental evaluation is conducted on two challenging face datasets for image-based age estimation extracted from the Internet Move Database (IMDB) and Wikipedia (WIKI). The results show the effectiveness of the proposed MNF-LRR.

scholarly journals Age estimation based on face images and pre-trained convolutional neural networks

2017 ◽  
Author(s):  
Abhinav Anand ◽  
Ruggero Donida Labati ◽  
Angelo Genovese ◽  
Enrique Munoz ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Face Images

scholarly journals Effective training of convolutional neural networks for age estimation based on knowledge distillation

2021 ◽  
Author(s):  
Antonio Greco ◽  
Alessia Saggese ◽  
Mario Vento ◽  
Vincenzo Vigilante
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Resource Constraints ◽  
Training Procedure ◽  
Face Images ◽  
Effective Training ◽  
Knowledge Distillation ◽  
Student Models ◽  
Teacher Model

AbstractAge estimation from face images can be profitably employed in several applications, ranging from digital signage to social robotics, from business intelligence to access control. Only in recent years, the advent of deep learning allowed for the design of extremely accurate methods based on convolutional neural networks (CNNs) that achieve a remarkable performance in various face analysis tasks. However, these networks are not always applicable in real scenarios, due to both time and resource constraints that the most accurate approaches often do not meet. Moreover, in case of age estimation, there is the lack of a large and reliably annotated dataset for training deep neural networks. Within this context, we propose in this paper an effective training procedure of CNNs for age estimation based on knowledge distillation, able to allow smaller and simpler “student” models to be trained to match the predictions of a larger “teacher” model. We experimentally show that such student models are able to almost reach the performance of the teacher, obtaining high accuracy over the LFW+, LAP 2016 and Adience datasets, but being up to 15 times faster. Furthermore, we evaluate the performance of the student models in the presence of image corruptions, and we demonstrate that some of them are even more resilient to these corruptions than the teacher model.

scholarly journals Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image

2021 ◽  
Vol 13 (7) ◽  
pp. 1372
Author(s):  
Jinhuan Xu ◽  
Liang Xiao ◽  
Jingxiang Yang
Keyword(s):  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Feature Space ◽  
Low Rank ◽  
Great Success ◽  
Clustering Methods ◽  
Hypergraph Learning ◽  
Optimal Dynamic ◽  
Low Rank Representation ◽  
Original Feature

Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information. Existing hypergraph learning methods only construct the hypergraph by a fixed similarity matrix or are adaptively optimal in original feature space; they do not update the hypergraph in subspace-dimensionality. In addition, the clustering performance obtained by the existing k-means-based clustering methods is unstable as the k-means method is sensitive to the initialization of the cluster centers. In order to address these issues, we propose a novel unified low-rank subspace clustering method with dynamic hypergraph for hyperspectral images (HSIs). In our method, the hypergraph is adaptively learned from the low-rank subspace feature, which can capture a more complex manifold structure effectively. In addition, we introduce a rotation matrix to simultaneously learn continuous and discrete clustering labels without any relaxing information loss. The unified model jointly learns the hypergraph and the discrete clustering labels, in which the subspace feature is adaptively learned by considering the optimal dynamic hypergraph with the self-taught property. The experimental results on real HSIs show that the proposed methods can achieve better performance compared to eight state-of-the-art clustering methods.

scholarly journals Facial Age Estimation Method Based on Fusion Classification and Regression Model

2018 ◽  
Vol 232 ◽  
pp. 02021
Author(s):  
Fengbing Jiang ◽  
Yu Zhang ◽  
GuoLiang Yang
Keyword(s):  
Regression Model ◽  
Age Estimation ◽  
Age Distribution ◽  
Estimation Method ◽  
Classification Model ◽  
Large Individual ◽  
Face Images ◽  
Classification And Regression ◽  
Better Than

Due to the large individual differences in the facial features of each person and the fact that the age has a certain time sequence, the age estimation based on face images faces certain difficulties. This article proposes a method based on fusion classification and regression model: A classification model and a regression model are added to the convolutional neural network to train the network under the premise of sharing convolutional layer parameters. The classification of the age of the label is used to code the age distribution, and the age is regressed using the Euclidean distance. The final predicted value of the model is the average of the two. Experiments show that the effect of fusion classification and regression model is better than that of a single model, which improves the accuracy of age estimation.

scholarly journals Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 146 ◽  
Author(s):  
Xinhua Liu ◽  
Yao Zou ◽  
Hailan Kuang ◽  
Xiaolin Ma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Data Augmentation ◽  
Rapid Development ◽  
Estimation Methods ◽  
Face Images

Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, human-computer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MA-SFV2: Mixed Attention-ShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the state-of-the-art.

scholarly journals Recursive Sample Scaling Low-Rank Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wenyun Gao ◽  
Xiaoyun Li ◽  
Sheng Dai ◽  
Xinghui Yin ◽  
Stanley Ebhohimhen Abhadiomhen
Keyword(s):  
Principal Components ◽  
Feature Space ◽  
Scaling Factor ◽  
Low Rank ◽  
Image Clustering ◽  
Subspace Segmentation ◽  
Clustering And Classification ◽  
Low Rank Representation ◽  
Segmentation Problem ◽  
Better Than

The low-rank representation (LRR) method has recently gained enormous popularity due to its robust approach in solving the subspace segmentation problem, particularly those concerning corrupted data. In this paper, the recursive sample scaling low-rank representation (RSS-LRR) method is proposed. The advantage of RSS-LRR over traditional LRR is that a cosine scaling factor is further introduced, which imposes a penalty on each sample to minimize noise and outlier influence better. Specifically, the cosine scaling factor is a similarity measure learned to extract each sample’s relationship with the low-rank representation’s principal components in the feature space. In order words, the smaller the angle between an individual data sample and the low-rank representation’s principal components, the more likely it is that the data sample is clean. Thus, the proposed method can then effectively obtain a good low-rank representation influenced mainly by clean data. Several experiments are performed with varying levels of corruption on ORL, CMU PIE, COIL20, COIL100, and LFW in order to evaluate RSS-LRR’s effectiveness over state-of-the-art low-rank methods. The experimental results show that RSS-LRR consistently performs better than the compared methods in image clustering and classification tasks.

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