Learning local features for age estimation on real-life faces

2010 ◽  
Author(s):  
Caifeng Shan
Keyword(s):  
Age Estimation ◽  
Real Life ◽  
Local Features

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.

Ensemble of Global and Local Features for Face Age Estimation

2011 ◽  
pp. 251-259 ◽  
Author(s):  
Wankou Yang ◽  
Cuixian Chen ◽  
Karl Ricanek ◽  
Changyin Sun
Keyword(s):  
Age Estimation ◽  
Local Features ◽  
Global And Local

scholarly journals Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment

2020 ◽  
Vol 10 (18) ◽  
pp. 6227
Author(s):  
Ebenezer Nii Ayi Hammond ◽  
Shijie Zhou ◽  
Hongrong Cheng ◽  
Qihe Liu
Keyword(s):  
Age Estimation ◽  
Real Life ◽  
Research Network ◽  
Superior Performance ◽  
Detection Scheme ◽  
Facial Landmark ◽  
Juvenile Age ◽  
Facial Shape ◽  
The Face ◽  
Reference Matrix

Facial age estimation is of interest due to its potential to be applied in many real-life situations. However, recent age estimation efforts do not consider juveniles. Consequently, we introduce a juvenile age detection scheme called LaGMO, which focuses on the juvenile aging cues of facial shape and appearance. LaGMO is a combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM). Inspired by the formation of words from morphemes, we obtained facial appearance features comprising facial shape and wrinkle texture and represented them as terms that described the age of the face. By leveraging the implicit ordinal relationship between the frequencies of the terms in the face, TF-IGM was used to compute the weights of the terms. From these weights, we built a matrix that corresponds to the possibilities of the face belonging to the age. Next, we reduced the reference matrix according to the juvenile age range (0–17 years) and avoided the exhaustive search through the entire training set. LaGMO detects the age by the projection of an unlabeled face image onto the reference matrix; the value of the projection depicts the higher probability of the image belonging to the age. With Mean Absolute Error (MAE) of 89% on the Face and Gesture Recognition Research Network (FG-NET) dataset, our proposal demonstrated superior performance in juvenile age estimation.

scholarly journals Age Estimation Using OLPP Features

2017 ◽  
Vol 10 (1) ◽  
pp. 238-248
Author(s):  
M. S Vaishnavi ◽  
A Vijayalakshmi
Keyword(s):  
Age Estimation ◽  
Binary Classification ◽  
Real Life ◽  
Support Vector ◽  
Vending Machines ◽  
Locality Preservation ◽  
Missing Children ◽  
Multi Stage ◽  
Sobel Edge Detection ◽  
Age Range

Aging face recognition poses as a key difficulty in facial recognition. It refers to identification of a person face over varied ages. It includes issues like age estimation, progression and verification. Non-availability of facial aging databases make it harder for any system to achieve good accuracy as there are no good training sets available. Age estimation when done correctly has a varied number of real life applications like age detailed vending machines, age specific access control and finding missing children. This paper implements age estimation using Park Aging Mind laboratory - Face database that contains metadata and 293 unique images of 293 individuals. Ages range from 19 to 45 with a median age of 32. Race is classified into two categories : African-American and Caucasian giving an accuracy of 98%. Sobel edge detection and Orthogonal locality preservation projection were used as the dominant features for the training and testing of age estimation. A Multi-stage binary classification using support vector machine was used to classify images into an age group thereafter predicting an individual’s age. The effectiveness of this method can be increased by using a large dataset with a wider age range.

Age Classification Using Motif and Statistical Features Derived On Gradient Facial Images

2020 ◽  
Vol 13 (5) ◽  
pp. 965-976
Author(s):  
Ayaluri Mallikarjuna Reddy ◽  
Vakulabharanam Venkata Krishna ◽  
Lingamgunta Sumalatha ◽  
Avuku Obulesh
Keyword(s):  
Age Estimation ◽  
Spatial Relations ◽  
Local Features ◽  
Initial Position ◽  
Age Group ◽  
Human Beings ◽  
Face Images ◽  
Estimation Operator ◽  
Age Group Classification ◽  
Occurrence Matrix

Background: Age estimation using face images has become increasingly significant in the recent years, due to diversity of potentially useful applications. Age group feature extraction, the local features, has received a great deal of attention. Objective: This paper derived a new age estimation operator called “Gradient Dual-Complete Motif Matrix (GD-CMM)” on the 3 x 3 neighborhood of gradient image. The GD-CMM divides the 3 x 3 neighborhood in to dual grids of size 2 x 2 each and on each 2 x 2 grid complete motif matrices are derived. Methods: The local features are extracted by using Motif Co-occurrence Matrix (MCM) and it is derived on 2 x 2 grid and the main disadvantage of this Motifs or Peano Scan Motifs (PSM) is they are static i.e. the initial position on a 2 x2 grid is fixed in deriving motifs, resulting with six different motifs. The advantage 3 x 3 neighborhood approaches over 2x 2 grids is the 3x3 grid identify the spatial relations among the pixels more precisely. The gradient images represent facial features more efficiently and human beings are more sensitive to gradient changes than original grey level intensities. Results: The proposed method is compared with other existing methods on FGNET, Google and scanned facial image databases. The experimental outcomes exhibited the superiority of proposed method than existing methods. Conclusion: On the GD-CMM, this paper derived co-occurrence features and machine learning classifiers are used for age group classification.

scholarly journals Age estimation by analysis of dental mineralization and its forensic contribution

2021 ◽  
Vol 10 (11) ◽  
pp. e598101119481
Author(s):  
Barbara Kuhnen ◽  
Clemente Maia da Silva Fernandes ◽  
Franciéllen de Barros ◽  
Júlia Moreira Andrade ◽  
José Scarso Filho ◽  
...  
Keyword(s):  
Age Estimation ◽  
Web Of Science ◽  
Real Life ◽  
Permanent Teeth ◽  
Court Cases ◽  
Dental Age ◽  
Dental Age Estimation ◽  
Specific Data ◽  
Endogenous Factors ◽  
Dental Mineralization

Age is an important factor in the formation of the uniqueness of individuals. The procedure for assessing age in situations that cannot determine chronological age, especially in court cases, is mandatory. The literature presents different methods to estimate the age of individuals because civil and criminal majority at 18 years is a milestone in Brazil and other countries. Thus, age estimation through the analysis of dental mineralization stages is important, as it is rarely affected by exogenous or endogenous factors. This study evaluates different methods used to estimate age through dental mineralization and its forensic contribution. The following databases were used: PubMed, Medline, Scopus, Web of Science, and Google Scholar, using the descriptors "age estimation", "dental age estimation", and "forensic dentistry", both isolated and combined. It was verified the reliability of the analysis of dental mineralization stages for age estimation. Some of the methods used for this purpose have not been tested in Brazilian individuals. There are no up-to-date data on mineralization stages of permanent teeth for this population. Thus, current and specific data from the Brazilian population are required because the results to be obtained from new studies could benefit society, assisting the clarification of Justice in real-life situations.

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