Human Age Prediction Based on Hand Image using Multiclass Classification

2020 ◽  
Author(s):  
Mohamed Ait Abderrahmane ◽  
Ibrahim Guelzim ◽  
Abdelkaher Ait Abdelouahad
Keyword(s):  
Multiclass Classification ◽  
Hand Image ◽  
Age Prediction

Gradient Texture Classification Based On Age Prediction of Face Images

2014 ◽  
Vol 1 (3) ◽  
pp. 23-31
Author(s):  
Basava Raju ◽  
◽  
K. Y. Rama Devi ◽  
P. V. Kumar ◽  
◽  
...  
Keyword(s):  
Texture Classification ◽  
Face Images ◽  
Age Prediction

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Modifying Perceived Size of a Handled Object through Hand Image Deformation

2013 ◽  
Vol 22 (3) ◽  
pp. 255-270 ◽  
Author(s):  
Yuki Ban ◽  
Takuji Narumi ◽  
Tomohiro Tanikawa ◽  
Michitaka Hirose
Keyword(s):  
Visual Representation ◽  
Shape Perception ◽  
Size Perception ◽  
Visual Object ◽  
Display System ◽  
Haptic Interaction ◽  
Image Deformation ◽  
Virtual Objects ◽  
The Difference ◽  
Hand Image

In this study, we aim to construct a perception-based shape display system to provide users with the sensation of touching virtual objects of varying shapes using only a simple mechanism. Thus far, we have proved that identified curved surface shapes or edge angles can be modified by displacing the visual representation of the user's hand. However, using this method, we cannot emulate multifinger touch, because of spatial unconformity. To solve this problem, we focus on modifying the identification of shapes using two fingers by deforming the visual representation of the user's hand. We devised a video see-through system that enables us to change the perceived shape of an object that a user is touching visually. The visual representation of the user's hand is deformed as if the user were handling a visual object; however, the user is actually handling an object of a different shape. Using this system, we conducted two experiments to investigate the effects of visuo-haptic interaction and evaluate its effectiveness. One is an investigation on the modification of size perception to confirm that the fingers did not stroke the shape but only touched it statically. The other is an investigation on the modification of shape perception for confirming that the fingers dynamically stroked the surface of the shape. The results of these experiments show that the perceived sizes of objects handled using a thumb and other finger(s) could be modified if the difference between the size of physical and visual stimuli was in the −40% to 35% range. In addition, we found that the algorithm can create an effect of shape perception modification when users stroke the shape with multiple fingers.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Self-weighted Robust LDA for Multiclass Classification with Edge Classes

2020 ◽  
Vol 12 (1) ◽  
pp. 1-19
Author(s):  
Caixia Yan ◽  
Xiaojun Chang ◽  
Minnan Luo ◽  
Qinghua Zheng ◽  
Xiaoqin Zhang ◽  
...  
Keyword(s):  
Multiclass Classification
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