scholarly journals Hybridized Feature Extraction for Facial Age Estimation using SVM

2019 ◽  
Vol 7 (11) ◽  
pp. 140-148
Author(s):  
Diri G. O.
Keyword(s):  
Feature Extraction ◽  
Age Estimation ◽  
Facial Age

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.

A Review of Different Techniques of Age Estimation from Human Face

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Khemchandra Patel ◽  
Dr. Kamlesh Namdev
Keyword(s):  
Age Estimation ◽  
Real Life ◽  
Extraction Methods ◽  
Human Face ◽  
Age Changes ◽  
Missing Children ◽  
Different Types ◽  
Age Variation ◽  
Human Faces ◽  
Facial Age

Age changes cause major variations in the appearance of human faces. Due to many lifestyle factors, it is difficult to precisely predict how individuals may look with advancing years or how they looked with "retreating" years. This paper is a review of age variation methods and techniques, which is useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation. Each has their own advantages and purpose. Because of its real life applications, researchers have shown great interest in automatic facial age estimation. In this paper, different age variation methods with their prospects are reviewed. This paper highlights latest methodologies and feature extraction methods used by researchers to estimate age. Different types of classifiers used in this domain have also been discussed.

Deep Appearance Model and Crow-Sine Cosine Algorithm-Based Deep Belief Network for Age Estimation

2021 ◽  
Vol 12 (3) ◽  
pp. 185-207
Author(s):  
Anjali A. Shejul ◽  
Kinage K. S. ◽  
Eswara Reddy B.
Keyword(s):  
Feature Extraction ◽  
Age Estimation ◽  
Learning Algorithm ◽  
Directional Pattern ◽  
Input Image ◽  
Deep Belief Network ◽  
Appearance Model ◽  
Belief Network ◽  
Sine Cosine Algorithm ◽  
New Feature

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).

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