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Algorithmic Solution for Determining the Age of a Person Based on 2D Photography Using Artificial Intelligence
Bednář, Pavel ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
Automated person's age estimation from a facial image is one of the challenges in the field of artificial intelligence and machine learning. Age estimation is often a non-trivial complexity for a person, unlike other biological characteristics such as determining gender or race. Information about an individual's age is very important for certain situations. For example, when committing an offense or crime, the amount of the sentence is co-determined by age. This information can also be used in the analysis of customers of a commercial entity and the subsequent adjustment of the offer. The aim of this work is to be able to extract his age from a photograph of a human face. The algorithm consists of two modules. If the first module says that the person is under 14 years old, the image will go to the second module. Furthermore, another version of the algorithm is proposed with an added module focused on selected facial features. In all modules transformations are performed on the image and their results are averaged. Finally, the algorithm is evaluated on standard datasets for age estimation and compared with state-of-the-art methods in this area.
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Detection of persons and evaluation of gender and age in image data
Dobiš, Lukáš ; Vičar, Tomáš (referee) ; Kolář, Radim (advisor)
Táto diplomová práca sa venuje automatickému rozpoznávaniu ludí v obrazových dátach s využitím konvolučných neurónových sieti na určenie polohy tváre a následnej analýze získaných dát. Výsledkom analýzy tváre je určenie pohlavia, emócie a veku osoby. Práca obsahuje popis použitých architektúr konvolučných sietí pre každú podúlohu. Sieť na odhad veku má natrénované nové váhy, ktoré sú vzápätí zmrazené a majú do svojej architektúry vložené LSTM vrstvy. Tieto vrstvy sú samostatne dotrénované a testované na novom datasete vytvorenom pre tento účel. Výsledky testov ukazujú zlepšenie predikcie veku. Riešenie pre rýchlu, robustnú a modulárnu detekciu tváre a ďalších ludských rysov z jedného obrazu alebo videa je prezentované ako kombinácia prepojených konvolučných sietí. Tieto sú implementované v podobe skriptu a následne vysvetlené. Ich rýchlosť je dostatočná pre ďalšie dodatočné analýzy tváre na živých obrazových dátach.
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Age prediction based on human face morphology
Žigová, Dominika ; Velemínská, Jana (advisor) ; Pilmann Kotěrová, Anežka (referee)
Age estimation is increasingly needed in numerous scientific disciplines, and thus the demand for appropriate age estimation methods is ever-growing. This master's thesis deals with age prediction based on the human facial morphology of people in the interval of 10 to 59 years. Three-dimensional virtual models of individuals of Czech, or Slovak, nationalities were used. The final sample for the thesis consists of 1046 3D facial scans, including 552 females and 494 males, for which age estimates were found using neural network models. Selected neural network models are based on two different approaches. While the PointNet, PointNet++, PointConv, and Xception networks use point clouds as input, the Multi- view Convolution Neural Network (MVCNN) utilizes multiple scan views. Point clouds were constructed from polygon meshes using uniform sampling of the mesh surface. In this case, models assess every single point. Therefore, a set containing the given object's 3D coordinates collected from its surface is obtained. Views of a particular scan result from recording a polygon mesh of the corresponding scan at a certain angle. This so-called multi-view approach is based on a projection, which records a 2D scan from various angles and then assesses and aggregates images into a general descriptor, which is...
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Age Estimation from Retinal Images: Different Image Preprocessing Approaches
Kadlec, Vojtěch
Human age is considered an important biometricparameter that is often difficult to determine. Previous studieshave shown that the non-specific general anatomical and physiologicalcharacteristics seen on fundus images are all likely signs ofageing. This paper focuses on age estimation from retinal imageswith different image preprocessing approaches together withproposed image detail enhancement method. Convolution neuralnetwork framework is based on the ResNet-34 architecturetogether with the Consistent Rank Logits algorithm estimatingage as an ordinal variable. The best model achieved a meanabsolute error of 3.47 years, outperforming existing modelsestimating age from retinal images.
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GAN Generated Data for CNN Age Estimation
Venkrbec, Tomáš ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
The goal of this thesis is to implement one of the state-of-the-art methods of generative adversarial networks and to propose its extension to conditional generation. This has been used to generate photorealistic images of human faces with specified characteristics such as age and gender. For this purpose, a highly diverse dataset of over 230,000 samples was created by merging and cleaning existing annotated face datasets. All ages, genders and different ethnic groups are well represented in it. StyleGAN2 generator trained on this dataset achieved a FID of 7.14. The synthetic data ratio was then experimented with during age classifier training. For the test subset of the dataset, the addition of synthetic data achieved a reduction in the mean absolute error from 3.499 years to 3.294 years. For the independent test dataset, a reduction in mean error from 4.012 years to 3.875 years was achieved.
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Age estimation from retinal images
Kadlec, Vojtěch ; Jakubíček, Roman (referee) ; Kolář, Radim (advisor)
Human age is considered an important biometric parameter that is often difficult to determine. Previous studies have shown that the non-specific general anatomical and physiological characteristics seen on fundus images are all likely signs of ageing. This bachelor thesis focuses on age estimation from retinal images. The first part of the thesis deals with the retina of the human eye as such, including physiological and pathological changes during aging. Then the principles of neural networks and principles of age classification from image data are described. The practical part is devoted to the implementation of the algorithm itself. The convolutional neural network framework is based on the ResNet-34 architecture together with the Consistent Rank Logits algorithm estimating age as an ordinal variable. The best model achieved a mean absolute error of 3.42 years, outperforming existing models estimating age from retinal images. All algorithms are implemented in the Python programming language using the Pytorch library.
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Geometric-morphometric approach to age and sex variability of the acetabulum
Cibulková, Simona ; Brůžek, Jaroslav (advisor) ; Bejdová, Šárka (referee)
6 Abstract This thesis compared age and sex differences in lunate surface morphology using a 3D geometric-morphometric approach. The acetabulum of 240 individuals was compared using landmarks and semilandmarks placed along the edge of the lunate surface. The individuals ranged in age from 20 to 90 and came from three geographic areas. This thesis was based on the study of San-Millán et al. (2017a) that used a 2D geometric-morphometric approach to investigate the shape of the acetabulum. Analyses in this thesis showed that size, sex, and age significantly affect the acetabular shape. The differences between both sexes can be observed in the size and depth of the acetabulum, the width of the acetabular notch, and the amount of bone growth at the acetabular horns and along the edges of the lunate surface. Both sexes exhibit age- related changes, which are linked to gradual deposits of bone along the edge of the lunate surface, the acetabular horns, and the acetabular fossa, which tends to lose the 3-lobed cloverleaf shape. According to the geometric-morphometric analysis conducted in this thesis, the acetabulum provides more accurate age estimates for individuals younger than 65 years of age. Keywords: Bioarchaeology, forensic anthropology, age estimation, sex estimation, acetabulum, lunate surface of hip...
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Utilization of forensic dentistry in indentification of individuals
Fialková, Martina ; Velemínská, Jana (advisor) ; Stránská, Petra (referee)
Forensic identification and age estimation has a significant role in cases when the unknown deceased body is found, after mass disasters when it is necessary to distinguish victims, but also in guestion of imigrants. And just these areas are very important part of forensic odontology, because dental development like a complex proces takes place from early foetal life to approximately 20 years of age is less affected by endocrine diseases or nutritional variations than other tissues. Dental age estimate is fundamental mainly in cases of children and young people, which teeth are still growing and they are in different developmental stages. On basis of these stages is possible to obtain very accurate results.
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Automated Human Recognition From Image Data
Dobiš, Lukáš
This paper describes an approach for automated human recognition by using convolutional neural networks (CNN) to perform facial analysis of persons face from image data. The predicted biometric indicators are following: age, gender, facial landmarks and facial expression. Network architectures with pretrained weights for each task are described. Script of interconnected CNN is explained and its results support further proposed expansion plans for live video inference.
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Algorithmic Solution for Determining the Age of a Person Based on 2D Photography Using Artificial Intelligence
Bednář, Pavel ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
Automated person's age estimation from a facial image is one of the challenges in the field of artificial intelligence and machine learning. Age estimation is often a non-trivial complexity for a person, unlike other biological characteristics such as determining gender or race. Information about an individual's age is very important for certain situations. For example, when committing an offense or crime, the amount of the sentence is co-determined by age. This information can also be used in the analysis of customers of a commercial entity and the subsequent adjustment of the offer. The aim of this work is to be able to extract his age from a photograph of a human face. The algorithm consists of two modules. If the first module says that the person is under 14 years old, the image will go to the second module. Furthermore, another version of the algorithm is proposed with an added module focused on selected facial features. In all modules transformations are performed on the image and their results are averaged. Finally, the algorithm is evaluated on standard datasets for age estimation and compared with state-of-the-art methods in this area.
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