scholarly journals Introduction to Human Age Estimation Using Face Images

2013 ◽  
Vol 21 (Special-Issue) ◽  
pp. 24-30 ◽  
Author(s):  
Petra Grd
Keyword(s):  
Age Estimation ◽  
Appearance Model ◽  
Age Group ◽  
Facial Image ◽  
Face Images ◽  
Human Age Estimation ◽  
Facial Images ◽  
Facial Age ◽  
Estimation Models

Abstract Age estimation is one of the tasks of facial image classification. It can be defined as determination of a person's age or age group from facial images. This paper gives an overview of recent research in facial age estimation. Along with an overview of previous research on this topic, descriptions of basic age estimation models are given: anthropometric model, active appearance model, aging pattern subspace and age manifold.

scholarly journals Analysis of Facial Images across Age Progression by Humans

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Jingting Zeng ◽  
Haibin Ling ◽  
Longin Jan Latecki ◽  
Shanon Fitzhugh ◽  
Guodong Guo
Keyword(s):  
Age Estimation ◽  
Age Group ◽  
Facial Image ◽  
Age Progression ◽  
Race And Gender ◽  
Age Gap ◽  
Human Ability ◽  
And Gender ◽  
Human Faces ◽  
Facial Images

The appearance of human faces can undergo large variations over aging progress. Analysis of facial image taken over age progression recently attracts increasing attentions in computer-vision community. Human abilities for such analysis are, however, less studied. In this paper, we conduct a thorough study of human ability on two tasks, face verification and age estimation, for facial images taken at different ages. Detailed and rigorous experimental analysis is provided, which helps understanding roles of different factors including age group, age gap, race, and gender. In addition, our study also leads to an interesting observation: for age estimation, photos from adults are more challenging than that from young people. We expect the study to provide a reference for machine-based solutions.

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).

scholarly journals Cross-Racial Automatic Age Estimation from Facial Images using Deep Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 1288-1294
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Learning Approach ◽  
Racial Groups ◽  
Human Beings ◽  
The Mean ◽  
Facial Images ◽  
Facial Age

This paper presents a deep learning approach for age estimation of human beings using their facial images. The different racial groups based on skin colour have been incorporated in the annotations of the images in the dataset, while ensuring an adequate distribution of subjects across the racial groups so as to achieve an accurate Automatic Facial Age Estimation (AFAE). The principle of transfer learning is applied to the ResNet50 Convolutional Neural Network (CNN) initially pretrained for the task of object classification and finetuning it’s hyperparameters to propose an AFAE system that can be used to automate ages of humans across multiple racial groups. The mean absolute error of 4.25 years is obtained at the end of the research which proved the effectiveness and superiority of the proposed method.

scholarly journals Gait-based human age estimation using age group-dependent manifold learning and regression

2018 ◽  
Vol 77 (21) ◽  
pp. 28333-28354 ◽  
Author(s):  
Xiang Li ◽  
Yasushi Makihara ◽  
Chi Xu ◽  
Yasushi Yagi ◽  
Mingwu Ren
Keyword(s):  
Manifold Learning ◽  
Age Estimation ◽  
Age Group ◽  
Human Age Estimation

AGE ESTIMATION USING NEURAL NETWORKS BASED ON FACE IMAGES WITH STUDY OF DIFFERENT FEATURE EXTRACTION METHODS

2017 ◽  
Vol 5 (2) ◽  
pp. 56-61
Author(s):  
Rajan Vishnu Parab ◽  
Meenal Suryakant Vatsaraj ◽  
D.S. Bade
Keyword(s):  
Feature Extraction ◽  
Age Estimation ◽  
Gabor Filter ◽  
Feature Representation ◽  
Training Data ◽  
Image Feature ◽  
Support Vector ◽  
Facial Image ◽  
Scattering Transform ◽  
Facial Age

 Facial age estimation recently becomes active research topic in pattern recognition. As there are vast potential application in age specific human computer interaction security control and surveillance monitoring. Insufficient and incomplete training data, uncontrollable environment, facial expression are the most prominent challenges in facial age estimation. Degree of accuracy for age estimation is obtained by forming appropriate feature vector of a facial image. Feature vectors are constructed from facial features. Therefore comparative study of feature extraction from facial image by bio inspired feature (BIF), histogram of gradient (HOG), Gabor filter, wavelet transform and scattering transform is done. The propose approach exploits scattering transform gives more information about features of the facial images. Well organized system consist scattering transform that disperse gabber coefficients pulling with smooth gaussian process in number of layers which isused to calculate for facial feature representation. These extracted features are classified using support vector machine and artificial neural network.

scholarly journals Facial age estimation using SFTA and deep neural network

2018 ◽  
Vol 7 (2) ◽  
pp. 281
Author(s):  
Deepa Nagarajan ◽  
T. Sasipraba
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Age Estimation ◽  
Deep Neural Network ◽  
Age Groups ◽  
Histogram Equalization ◽  
Age Group ◽  
Final Layer ◽  
Feature Values ◽  
Facial Age

This paper construes the toils in facial age estimation in images. The fact that manual age estimation is indeed hard rising out the urge for digital age estimation. To make estimation precise many works have been carried out by considering a lot of constraints. In this paper, facial age estimation is done more accurately. SFTA method is used for feature extraction and meticulous results are obtained for all age groups. Histogram equalization is done using the Otsu algorithm and three layered Deep Neural Network is used to classify the age group. In a Deep neural network, softmax normalization is done in the final layer to preserve the outlier values. By extracting 45 feature values concerning color and gradient, key point descriptor, orientation, shape and texture better estimation are obtained.

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