Modern machine learning techniques (such as deep learning) offer immense opportunities
in the field of human biological aging research. Aging is a complex process, experienced by …

Human age estimation is an important and difficult challenge. Different biomarkers and
numerous approaches have been studied for biological age estimation, each with its …

Age-related physiological changes in humans are linearly associated with age. Naturally,
linear combinations of physiological measures trained to estimate chronological age have …

Abstract Deep Neural Networks (DNN) have been recently developed for the estimation of
Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its …

Age estimation from face images is an important yet difficult task in computer vision. Its main
difficulty lies in how to design aging features that remain discriminative in spite of large facial …

Multiple recent advances in machine learning enabled computer systems to exceed human
performance in many tasks including voice, text, and speech recognition and complex …

Age estimation of unknown persons is a challenging pattern analysis task due to the lack of
training data and various ageing mechanisms for different individuals. Label distribution …

Human age provides key demographic information. It is also considered as an important soft
biometric trait for human identification or search. Compared to other pattern recognition …

Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

Healthy aging is the prime goal of aging research and interventions. Healthy aging or not
can be quantified by biological aging rates estimated by aging clocks. Generation and …