Development Study of Deep Learning Facial Age Estimation

Human age estimation is one of the most challenging problem because it can be used in many applications relating to age such as age-specific movies, age-specific computer applications or website, etc. This paper will contribute to give brief information about development of age estimation researches using deep learning. We explore three recent journal papers that give significant contribution in age estimation using deep learning. From these papers, they selected classification methods and there is gradual improvement in result and also in selected loss function. The best result gives MAE (mean average error) 2.8 years and VGG-16 is the most selected CNN architecture

Deep Learning for Brain Age Estimation: A Systematic Review

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning model

Forensic Dental Age Estimation Using Modified Deep Learning Neural Network

Dental age is one of the most reliable methods to identify an individual's age. By using dental panoramic radiography (DPR) images, physicians and pathologists in forensic sciences try to establish the chronological age of individuals with no valid legal records or registered patients. The current methods in practice demand intensive labor, time, and qualified experts. The development of deep learning algorithms in the field of medical image processing has improved the sensitivity of predicting truth values while reducing the processing speed of imaging time. This study proposed an automated approach to estimate the forensic ages of individuals ranging in age from 8 to 68 using 1,332 DPR images. Initially, experimental analyses were performed with the transfer learning-based models, including InceptionV3, DenseNet201, EfficientNetB4, MobileNetV2, VGG16, and ResNet50V2; and accordingly, the best-performing model, InceptionV3, was modified, and a new neural network model was developed. Reducing the number of the parameters already available in the developed model architecture resulted in a faster and more accurate dental age estimation. The performance metrics of the results attained were as follows: mean absolute error (MAE) was 3.13, root mean square error (RMSE) was 4.77, and correlation coefficient R$^2$ was 87%. It is conceivable to propose the new model as potentially dependable and practical ancillary equipment in forensic sciences and dental medicine.Comment: 18 pages, 10 figures, 3 table

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity

Human age estimation is an important and difcult challenge. Diferent biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activity can be exploited for biological age estimation for adult humans. We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predict biological age, using human physical activity as recorded by a wearable device. We also demonstrate fve deep biological age estimation models including the proposed approach and compare their performance on the NHANES physical activity dataset. Results on mortality hazard analysis using both the Cox proportional hazard model and Kaplan-Meier curves each show that the proposed method for estimating biological age outperforms other state-of-the-art approaches. This work has signifcant implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment. Mobile health (mHealth) applications provide patients, caregivers, and administrators continuous information about a patient, even outside the hospital

흉부 방사선 사진을 활용한 인공지능 기반 노령견 나이 추정에 관한 연구

학위논문(석사) -- 서울대학교대학원 : 수의과대학 수의학과, 2023. 2. 최지혜.Background: Age is a vital information that impacts all facets of veterinary medicine and accurate estimation of age is needed in fields such as emergency and shelter medicine. A few studies involving age estimation based on deep learning of medical images exists in human medicine, however, in dogs automatic age prediction with machine learning has been based solely on non-medical portrait images. Purpose: This study proposes a deep learning solution for age estimation in geriatric dogs from thoracic radiographs and sets the stage for future deep learning research involving other medical imaging modalities and for development of age-related biomarkers. Method: A large dataset of canine thoracic radiographs was utilized to train and test the convolutional neural networks performance in the estimation of age based on performance metric mean absolute error. Results: The network was able to extract age-related information from thoracic radiographs of geriatric dogs to estimate age with moderate correlation to the ground truth and through the analysis of activation maps, the vertebra was identified as the main region containing age-related information consistent with previously known sites of degenerative changes. Conclusion: The convolutional neural networks feasibility in geriatric age estimation using canine thoracic radiographs was confirmed by the results of this study and allowed visualization of areas that may contain age-related information deciphered by artificial intelligence.연령은 수의학의 모든 측면에 영향을 미치는 중요한 정보이며, 이 연구는 개 흉부 방사선 영상을 사용하여 노령견들의 연령 추정을 하는 deep learning solution을 제안합니다. 이 연구는 나이 추정을 위해 컨볼루션 신경망을 학습시키고 성능을 시험하기 위해 공개적으로 사용 가능한 대규모 방사선 데이터 세트와 동물 의료 기관의 추가 데이터를 사용하여 수행되었습니다. 이 네트워크는 흉부 방사선 영상에서 연령 관련 정보를 추출하여 상당히 정확하게 노령견에서 연령들을 추정할 수 있었습니다. 또한 활성화 맵 분석을 통해 축근골격계가 연령 관련 정보를 포함하는 주요 영역으로 식별되었으며 이 결과는 이전 연구들에서 알려졌던 축근골격계에서 나타나는 퇴행성 변화들을 네트워크가 인식했을 가능성을 제시합니다. 흉부 방사선 촬영은 여전히 호흡기 및 심혈관 질환을 선별하기 위해 수의학에서 가장 자주 사용되는 영상 검사 중 하나이기 때문에 동물보호소 의학, 예방 의학 및 반려 동물 보험과 같은 영역에서 자동 연령 추정의 실제 사용을 기대할 수 있습니다. 본연구는 딥 러닝과 의료 영상을 연령 추정에 활용해본 수의학 분야 최초의 연구로서, 향후 소동물의학 분야에서 컨볼루션 신경망을 활용한 연령 측정 및 연령 관련 바이오마커 개발 연구등의 발판을 마련하고자 합니다.Introduction 1 Materials and methods 5 1. Dataset 5 2. Hardware specification 8 3. Training, validation, and testing 9 4. Statistical correlation analysis 11 5. Activation map quantification 13 Results 14 Discussion 27 Conclusion 30 References 31 국문초록 40석

Age Estimation using Deep Learning on 3D Facial Features

Intelligent Systems are designed to substitute the human component therefore they have a need to emulate a human's ability to quickly estimate biological traits of others, which is an integral part of social interactions. Age is one of the key characteristics used by marketing, entertainment and security tools. Existing age estimation systems can be easily fooled due to their reliance on human appearance based features, which can be easily manipulated. Over the years, while the complexity of models increased, the data fed to our systems was kept the same: a single 2D RGB image. This thesis addresses the current lack of studies made on the uses of 3D facial information ion the context of age estimation. This thesis encompasses a comprehensive study of how different 3D facial features can be used to improve current state of the art age estimation approaches using Deep Learning. Along with extensions to a baseline Convolutional Neural Network (CNN) model with a 2D image input, it is introduced a novel Multi-View CNN model which combines face descriptors from multiple perspectives within the model's architecture. Due to lack of 3D facial datasets aimed at age estimation, 2D age estimation datasets were synthetically augmented with landmark localization, 3DMM parametrization and 3D facial point cloud reconstruction. The last one was subsequently used to create a new synthetic dataset composed of renderings of each point cloud from different camera positions. A fully customizable data processing tool was introduced which supports image pre-processing, dataset splitting, image augmentation and synthetic feature extraction. Quantitative results show improvement of the 3D methods over traditional 2D although somewhat constrained by data quality

A Multi-featured Approach by Integrating Digital Hand and Dental X-Ray for Human Age Estimation

Traditionally, human bone age is estimated manually by inspecting the multiple body part X-ray images, which is extremely time-consuming and prone to error. The accuracy of the human estimate depends on the experience of the medical practitioner, and thus it suffers from intra- and inter-observer variability. Hence, efficient automatic approaches are required to determine human age with high accuracy. In this work, we propose a human age estimation technique using Deep Learning (DL) technique based on hand X-ray images combined with dental orthopantomographs (OPGs) is proposed. Here, the input X-ray image is pre-processed first using Non-Local Means (NLM) first, followed by Region of Interest (RoI) extraction. Later, color and position image augmentation are performed in order to balance the dataset. Thereafter, the salient features in the image are determined, and based on these features, human age estimation is carried out using the Deep Residual Network (DRN). Here, the DRN is trained using the Beluga whale lion optimization (BWLO) algorithm. Furthermore, the BWLO_DRN is examined for its superiority considering the model accuracy and is found to obtain value of 90.1% on hand-wrist and 89.9% OPG real time dataset, thus showing superior performance for hand-wrist images