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 Estimation of Age Using Dimensions of Apical Foramina of Teeth

2021 ◽  
Vol 10 (10) ◽  
pp. 705-710
Author(s):  
Aklesha Behera ◽  
Archana Santhanam ◽  
Herald J. Sherlin ◽  
Gifrina Jayaraj ◽  
Don K.R.
Keyword(s):  
Age Estimation ◽  
Age Groups ◽  
Age Determination ◽  
Forensic Odontology ◽  
Estimation Methods ◽  
P Value ◽  
Age Group ◽  
Human Beings ◽  
Mean Diameter ◽  
The Mean

BACKGROUND Forensic odontology is an ever growing and a flourishing science. The science deals in criminal laws by using dental knowledge. People who practice forensic odontology are called as forensic dentists. They are asked to determine the age, sex, race, occupation, previous dental history and various other determinants like DNA verification in unidentified human beings. Teeth are a very special tissue in the human body. Teeth are the only tissue which can survive through extreme climatic and environmental conditions like heat, cold etc. hence many a times retains its morphology. Age estimation has been studied by many researchers but age estimation methods have been incapable of providing the age estimation in older age groups. The study aims to determine the age using the dimensions of the apical foramen from middle age to old age groups. METHODS A descriptive ex vivo study, was done from December 2019 to March 2020. Freshly extracted teeth were collected and cleaned using hydrogen peroxide. The teeth selected were single rooted teeth like incisors, canine and premolars in the age group of 30 to 40 years, 41 - 50 years, 51 - 60 years and 61 - 70 years. Apical one third of the teeth was sectioned, mounted over a slide and placed perpendicular to the lens of the stereomicroscope. Using Mag Vision and Image J software, photomicrographs and measurements were taken. Data procured was tabulated and statistical analysis was done using Statistical Package for the Social Sciences (SPSS) version 20. RESULTS In 30 - 40 years age group, the mean diameter of all single rooted teeth was 0.35 mm. Among the 41 - 50 years age group, the mean diameter was 0.43mm. In 51 - 60 years age group it was 0.41mm and in the 61 - 70 years age group, the mean diameter was 0.36 mm. Pearson chi square P value for incisor is 0.280, canine is 0.223 and premolar is 0.326. CONCLUSIONS Age estimation can be done using physiological dimensions of apical foramen. From the present study, dimensions of apical foramen decreases with increasing age due to cementum deposition. KEY WORDS Age Determination, Apical Foramen Diameter, Single Rooted Teeth

scholarly journals Age-group Classification Using 3DHOG Descriptor Applied to Depth Maps

10.29007/rvq6 ◽  
2020 ◽  
Author(s):  
Nabila Mansouri ◽  
Hana Bougueddima ◽  
Yousra Ben Jemaa
Keyword(s):  
Age Estimation ◽  
Region Of Interest ◽  
Depth Map ◽  
Aging Effect ◽  
Customer Relationship ◽  
Age Group ◽  
Depth Maps ◽  
Security Control ◽  
Age Group Classification ◽  
Public Datasets

Age estimation has lots of real-world applications, such as security control, biometrics, customer relationship management, entertainment and cosmetology. In fact, facial age estimation has gained wide popularity in recent years. Despite numerous research efforts and advances in the last decade, traditional human age-group recognition with the sequence of 2D color images is still a challenging problem. The goal of this work is to recognize human age-group only using depth maps without additional joints information. As a practical solution, we present a novel representation of global appearance of aging-effect such as wrinkles’ depth. The proposed framework relay, first-of-all, on an extended version of Viola-Jones algorithm for face and region of interest (most affected by aging) extraction. Then, the 3D histogram of oriented gradients is used to describe local appearances and shapes of the depth map, for more compact and discriminative aging effect representation. The presented method has been compared with the state-of-the-art 2D-approaches on public datasets. The experimental results demonstrate that our approach achieves a better and more stable performances.

scholarly journals Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU) and Fuzzy Texture

2019 ◽  
pp. 605-611
Author(s):  
Jammula Nimitha ◽  
Kuraganti Nukeswari ◽  
Kuraganti Sudha ◽  
Kurapati Sumithra ◽  
Rama Devi Gunnam
Keyword(s):  
Age Groups ◽  
Group Classification ◽  
Facial Features ◽  
Age Group ◽  
Human Beings ◽  
Age Group Classification ◽  
Facial Images

We as human beings can estimate the age of a person based on his facial features but there are situations where there is a need for the computers to determine the age of a person based on the picture or photograph. Here comes the situation to teach a machine to determine the age group of a person with his picture. This is applicable in the fields like determining the age of a criminal with his picture or determining the age of a patient when he has undergone an accident and many other fields. To address this problem the paper proposed a technique of finding the age with Rank Based Edge Texture Unit (RETU). The uniqueness of this method is that it divides the age group into 7 classes i.e. the age groups are 1-10, 11-20, 21-30,31-0,41-50,51-60,>60 . With this method, the results cope up to 97.16% and to slightly increase the efficiency the present paper proposes to add Fuzzy Texton features.

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.

COMPARISION OF DENTAL SCORING SYSTEM WITH RADIOGRAPHIC SKELETAL SYSTEM IN AGE ESTIMATION

JKCD ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 9-11
Author(s):  
Sadaf Ambreen
Keyword(s):  
Age Estimation ◽  
Scoring System ◽  
Skeletal Age ◽  
Chronological Age ◽  
Age Group ◽  
Dental Age ◽  
Scoring Method ◽  
Cross Sectional ◽  
Study Results ◽  
Male Subjects

Objectives: To compare Demirjian Dental scoring method with Greulich-Pyle (GP) Skeletal method of age estimation in pubertal children. Materials and Methods: Sample of the study included 267 male healthy subjects of 11-16 years of age group.. Demirjian Scoring system was utilized to evaluate the orthopantomograms to assess their Dental age and the Hand-Wrist radiographs were analyzed to calculate the skeletal age by utilizing GP atlas. Chronological age was obtained from the date of birth of the subject .Both methods were compared with one another and with the chronological age. It was a cross-sectional study and only healthy male subjects without any clinical abnormalities were included in the study. Results: A total of 267 male subjects of 11-16 years of age group were assessed by Demirjian and Greulich Pyle Methods. Both were compared with Chronological Age. Data obtained was statistically analyzed and the Student “t” test was applied in the study population. The mean difference between Chronolgical age and dental age was 0.69years and that of chronological age and skeletal age was 0.87 years. It was observed from dental age assessment that it does not differ much from the skeletal age. Conclusion: It was concluded that Demirjian method of Age Estimation is more precise than Greulich Pyle method of Age Estimation. Furthermore both methods can be used selectively in Medicolegal cases to access bone age which can be easily correlated to chronological age.

Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

2021 ◽  
pp. 1-17
Author(s):  
Khaled ELKarazle ◽  
Valliappan Raman ◽  
Patrick Then
Keyword(s):  
Age Estimation ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Age Group Classification ◽  
Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

scholarly journals Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Front Face Images

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 385 ◽  
Author(s):  
Yoosoo Jeong ◽  
Seungmin Lee ◽  
Daejin Park ◽  
Kil Park
Keyword(s):  
Age Estimation ◽  
Metric Learning ◽  
Front Face ◽  
Experimental Fact ◽  
Gender Classification ◽  
Learning Method ◽  
Siamese Network ◽  
Task Learning ◽  
Face Images ◽  
Deep Metric Learning

Recently, there have been many studies on the automatic extraction of facial information using machine learning. Age estimation from front face images is becoming important, with various applications. Our proposed work is based on the binary classifier, which only determines whether two input images are clustered in a similar class, and trains the convolutional neural networks (CNNs) model using the deep metric learning method based on the Siamese network. To converge the results of the training Siamese network, two classes, for which age differences are below a certain level of distance, are considered as the same class, so the ratio of positive database images is increased. The deep metric learning method trains the CNN model to measure similarity based on only age data, but we found that the accumulated gender data can also be used to compare ages. From this experimental fact, we adopted a multi-task learning approach to consider the gender data for more accurate age estimation. In the experiment, we evaluated our approach using MORPH and MegaAge-Asian datasets, and compared gender classification accuracy only using age data from the training images. In addition, from the gender classification, we found that our proposed architecture, which is trained with only age data, performs age comparison by using the self-generated gender feature. The accuracy enhancement by multi-task learning, for the simultaneous consideration of age and gender data, is discussed. Our approach results in the best accuracy among the methods based on deep metric learning on MORPH dataset. Additionally, our method is also the best results compared with the results of the state of art in terms of age estimation on MegaAge Asian and MORPH datasets.

scholarly journals Establishment of age group classification for risk stratification in glioma patients

2020 ◽  
Author(s):  
Zhiying Lin ◽  
Runwei Yang ◽  
Yawei Liu ◽  
Kaishu Li ◽  
Guozhong Yi ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Risk Stratification ◽  
Tumor Size ◽  
Age Groups ◽  
Tumor Location ◽  
Group Classification ◽  
Age Group ◽  
Ki 67 ◽  
Who Grade ◽  
Age Group Classification

Abstract Objective: Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. Methods: A total of 1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation direction, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, p53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, 1p/19q, PR, CD3, H3K27M, and TS. Results: The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0-14 years old (pediatric group), 15-47 years old (youth group), 48-63 years old (middle-aged group), and ≥64 years old (elderly group).The proportions of glioblastoma and large tumor size (4-6 cm) increased with age (p = 0.000, p = 0.018, respectively ). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p = 0.000, p = 0.017, respectively). Conclusion: Age was effective evaluating the risk of glioblastoma in glioma patients. Appropriate classifications of age group for risk stratification were 0-14 years old (pediatric group), 15-47 years old (young group), 48-63 years old (middle age group) and ≥ 64 years old (elderly group). There was significant heterogeneity in WHO grade, tumor size, tumor location and some molecular markers among the four age groups.

scholarly journals Age estimation via face images: a survey

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Raphael Angulu ◽  
Jules R. Tapamo ◽  
Aderemi O. Adewumi
Keyword(s):  
Age Estimation ◽  
Face Images
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