scholarly journals Automatic Age Estimation System for Face Images

10.5772/52862 ◽  
2012 ◽  
Vol 9 (5) ◽  
pp. 216 ◽  
Author(s):  
Chin-Teng Lin ◽  
Dong-Lin Li ◽  
Jian-Hao Lai ◽  
Ming-Feng Han ◽  
Jyh-Yeong Chang
Keyword(s):  
Age Estimation ◽  
Face Images ◽  
Estimation System

Automatic Human Age Estimation System for Face Images

2018 ◽  
Vol 6 (7) ◽  
pp. 550-555
Author(s):  
Mittala Thulasi ◽  
Chandra Mohan Reddy Sivappagari
Keyword(s):  
Age Estimation ◽  
Face Images ◽  
Human Age Estimation ◽  
Estimation System

scholarly journals Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Front Face Images

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 385 ◽  
Author(s):  
Yoosoo Jeong ◽  
Seungmin Lee ◽  
Daejin Park ◽  
Kil Park
Keyword(s):  
Age Estimation ◽  
Metric Learning ◽  
Front Face ◽  
Experimental Fact ◽  
Gender Classification ◽  
Learning Method ◽  
Siamese Network ◽  
Task Learning ◽  
Face Images ◽  
Deep Metric Learning

Recently, there have been many studies on the automatic extraction of facial information using machine learning. Age estimation from front face images is becoming important, with various applications. Our proposed work is based on the binary classifier, which only determines whether two input images are clustered in a similar class, and trains the convolutional neural networks (CNNs) model using the deep metric learning method based on the Siamese network. To converge the results of the training Siamese network, two classes, for which age differences are below a certain level of distance, are considered as the same class, so the ratio of positive database images is increased. The deep metric learning method trains the CNN model to measure similarity based on only age data, but we found that the accumulated gender data can also be used to compare ages. From this experimental fact, we adopted a multi-task learning approach to consider the gender data for more accurate age estimation. In the experiment, we evaluated our approach using MORPH and MegaAge-Asian datasets, and compared gender classification accuracy only using age data from the training images. In addition, from the gender classification, we found that our proposed architecture, which is trained with only age data, performs age comparison by using the self-generated gender feature. The accuracy enhancement by multi-task learning, for the simultaneous consideration of age and gender data, is discussed. Our approach results in the best accuracy among the methods based on deep metric learning on MORPH dataset. Additionally, our method is also the best results compared with the results of the state of art in terms of age estimation on MegaAge Asian and MORPH datasets.

scholarly journals Age estimation via face images: a survey

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Raphael Angulu ◽  
Jules R. Tapamo ◽  
Aderemi O. Adewumi
Keyword(s):  
Age Estimation ◽  
Face Images

scholarly journals Apparent Age Estimation System Based on Age Perception

10.5772/13946 ◽  
2011 ◽  
Author(s):  
Hironobu Fukai ◽  
Hironori Takimoto ◽  
Yasue Mitsukura ◽  
Minoru Fukumi
Keyword(s):  
Age Estimation ◽  
Age Perception ◽  
Estimation System ◽  
Apparent Age

scholarly journals A Hierarchical Framework for Facial Age Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuyu Liang ◽  
Xianmei Wang ◽  
Li Zhang ◽  
Zhiliang Wang
Keyword(s):  
Age Estimation ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Age Group ◽  
K Nearest Neighbor ◽  
Complex Issue ◽  
Ordinal Information ◽  
Hierarchical Framework ◽  
Estimation System

Age estimation is a complex issue of multiclassification or regression. To address the problems of uneven distribution of age database and ignorance of ordinal information, this paper shows a hierarchic age estimation system, comprising age group and specific age estimation. In our system, two novel classifiers, sequence k-nearest neighbor (SKNN) and ranking-KNN, are introduced to predict age group and value, respectively. Notably, ranking-KNN utilizes the ordinal information between samples in estimation process rather than regards samples as separate individuals. Tested on FG-NET database, our system achieves 4.97 evaluated by MAE (mean absolute error) for age estimation.

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

A Design of an Age Estimation System Using the SOM

2006 ◽  
Author(s):  
Hironobu Fukai ◽  
Yasue Mitsukura ◽  
Hironori Takimoto ◽  
Minoru Fukumi
Keyword(s):  
Age Estimation ◽  
Estimation System
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