Deep Learning with PCANet for Human Age Estimation

2016 ◽  
pp. 300-310 ◽  
Author(s):  
DePeng Zheng ◽  
JiXiang Du ◽  
WenTao Fan ◽  
Jing Wang ◽  
ChuanMin Zhai
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Human Age Estimation

Human Age Estimation Using Deep Learning from Gait Data

2021 ◽  
pp. 281-294
Author(s):  
Refat Khan Pathan ◽  
Mohammad Amaz Uddin ◽  
Nazmun Nahar ◽  
Ferdous Ara ◽  
Mohammad Shahadat Hossain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Human Age Estimation

scholarly journals USING SUPERVISED DEEP LEARNING FOR HUMAN AGE ESTIMATION PROBLEM

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 97-100 ◽  
Author(s):  
K. A. Drobnyh ◽  
A. N. Polovinkin
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Estimation Algorithms ◽  
Broad Variety ◽  
Human Age Estimation ◽  
Deep Learning Features ◽  
Active Appearance ◽  
Improved Accuracy

Automatic facial age estimation is a challenging task upcoming in recent years. In this paper, we propose using the supervised deep learning features to improve an accuracy of the existing age estimation algorithms. There are many approaches solving the problem, an active appearance model and the bio-inspired features are two of them which showed the best accuracy. For experiments we chose popular publicly available FG-NET database, which contains 1002 images with a broad variety of light, pose, and expression. LOPO (leave-one-person-out) method was used to estimate the accuracy. Experiments demonstrated that adding supervised deep learning features has improved accuracy for some basic models. For example, adding the features to an active appearance model gave the 4% gain (the error decreased from 4.59 to 4.41).

Complexities and considerations of human age estimation

2019 ◽  
pp. 1-15
Author(s):  
Sarah Ellingham ◽  
Joe Adserias-Garriga
Keyword(s):  
Age Estimation ◽  
Human Age Estimation

Methods and markers used for human age estimation in forensics

2016 ◽  
Vol 294 ◽  
pp. 105-109
Author(s):  
Anna Woźniak ◽  
Keyword(s):  
Biological Material ◽  
Age Estimation ◽  
Prediction Error ◽  
Estimation Techniques ◽  
New Methods ◽  
Human Age Estimation

The article presents the previous and current methods and markers used for estimation of human age. The analysis of biological material recovered from the scene of the event makes it possible to estimate the age of a person who deposited traces. The new methods allow determining the depositor’s age, based on biological traces commonly found at the scene, such as blood, saliva or sperm, with an accuracy of a few years. The previously used age estimation techniques required larger quantities of biological material, whereas their prediction error amounted to even several decades.

Forensic age estimation for pelvic X-ray images using deep learning

2018 ◽  
Vol 29 (5) ◽  
pp. 2322-2329 ◽  
Author(s):  
Yuan Li ◽  
Zhizhong Huang ◽  
Xiaoai Dong ◽  
Weibo Liang ◽  
Hui Xue ◽  
...  
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Forensic Age Estimation ◽  
X Ray
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