scholarly journals Semantic Human Face Analysis for Multi-level Age Estimation

2022 ◽  
Vol 31 (1) ◽  
pp. 555-580
Author(s):  
Rawan Sulaiman Howyan ◽  
Emad Sami Jaha
Keyword(s):  
Age Estimation ◽  
Human Face ◽  
Face Analysis ◽  
Multi Level

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.

scholarly journals Age Estimation with Smartphone: Is It Reliable for Forensics Identification?

2020 ◽  
Vol 23 (2) ◽  
pp. 34-38
Author(s):  
Mieke Sylvia Margaretha Amiatun Ruth ◽  
Novita ◽  
Levina Gita ◽  
Arofi Kurniawan ◽  
Haryono Utomo
Keyword(s):  
Age Estimation ◽  
Personal Identification ◽  
Forensic Identification ◽  
Human Face ◽  
Biological Profile ◽  
Age Progression ◽  
Pollution Research ◽  
Technology Age ◽  
Profile Reconstruction ◽  
Research Cost

Age estimation is one of the important components in forensic science used for personal identification, biological profile reconstruction, and help narrowing the search possibilities. Age estimation can be done by various methods and biological evidence, such as the human face. The human face is one of biometrics that provides a variety of information.  The purpose of this article is to evaluate the accuracy and reliability of age estimation with face using smartphone for forensic identification based on previous studies and experiences. Age estimation by face is based on age progression that causes attrition and degeneration on soft tissue. With the development of technology, age estimation by face can be done with applications or websites on smartphone. In general, the utilization of smartphone can reduce waste, pollution, research cost and easier to save and share. A lot of applications have been developed and free to download. Unfortunately, the accuracy of its results is unknown. In conclusion, the applications for age estimation on smartphone give quiet good results and can be used as a supporting tool to estimate age in forensic identification.  

Deep Multi-level Feature Fusion Network for Age Estimation

2021 ◽  
Author(s):  
Liming Chen ◽  
Qiang Jiang ◽  
Chuan Huang
Keyword(s):  
Age Estimation ◽  
Feature Fusion ◽  
Multi Level

FEATURE EXTRACTION THROUGH CROSS-PHASE CONGRUENCY FOR FACIAL EXPRESSION ANALYSIS

2009 ◽  
Vol 23 (03) ◽  
pp. 617-635 ◽  
Author(s):  
IOAN BUCIU ◽  
IOAN NAFORNITA
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Task ◽  
Component Analysis ◽  
Support Vector ◽  
Expression Recognition ◽  
Human Face ◽  
Phase Congruency ◽  
Face Analysis ◽  
Linear Discriminant

Human face analysis has attracted a large number of researchers from various fields, such as computer vision, image processing, neurophysiology or psychology. One of the particular aspects of human face analysis is encompassed by facial expression recognition task. A novel method based on phase congruency for extracting the facial features used in the facial expression classification procedure is developed. Considering a set of image samples comprising humans expressing various expressions, this new approach computes the phase congruency map between the samples. The analysis is performed in the frequency space where the similarity (or dissimilarity) between sample phases is measured to form discriminant features. The experiments were run using samples from two facial expression databases. To assess the method's performance, the technique is compared to the state-of-the art techniques utilized for classifying facial expressions, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and Gabor jets. The features extracted by the aforementioned techniques are further classified using two classifiers: a distance-based classifier and a Support Vector Machine-based classifier. Experiments reveal superior facial expression recognition performance for the proposed approach with respect to other techniques.

Human Face Analysis with Nonlinear Manifold Learning

2011 ◽  
Vol 33 (10) ◽  
pp. 2531-2535 ◽  
Author(s):  
Xiao-kan Wang ◽  
Xia Mao ◽  
Ishizuka Mitsuru
Keyword(s):  
Manifold Learning ◽  
Human Face ◽  
Face Analysis
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