Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

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.

Age Group Classification using Convolutional Neural Network (CNN)

Age group classification is a complex task that is used to classify facial images or videos into predetermined age categories. It is an important task due to its numerous applications such as health, security, authentication system, recruitment, and also in intelligent social robots. Convolutional Neural Network (CNN) has recently shown excellent performance in analysing human face images and videos. This paper proposed an age group classification task using CNN that trained and tested with an All-Age Face (AAF) dataset. FaceNet deep learning model that uses CNN was applied in this study to compute a 128-d embedding that quantifies the face of the age group. The experiment included two age groups: Adolescence and Mature Adulthood. The proposed age group classification model achieved 84.90% accuracy for the training images and 85.12% accuracy for the test images. The experimental results showed that CNN is capable of achieving competitive classification accuracy throughout two age groups in the AAF dataset with unbalanced data distribution.

Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application

The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.

Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma

Background Malignant brain tumor diseases exhibit differences within molecular features depending on the patient’s age. Methods In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers. Results Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification. Conclusions We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.

Age group classification to identify the progress of language development based on convolutional neural networks

Speech pathology is a scientific study of speech disorders. In this field, the study also analyzes and evaluates language abilities for the purpose of improving speech and hearing. Speech therapy first performs evaluation of speech ability, which is expensive. In order to solve this problem, software methodologies have been applied to language analysis, but most of them have been applied to only part of the whole process. In this study, the degree of language development is judged by determining the age group of the speaker (Pre-school children, Elementary school, Middle and high school, Adults, and Senior citizen) using deep learning and simple statistics. We use transcription data from the counseling contents and multi-kernel CNN model. At this time, in order to understand the characteristics of Korean language belonging agglutinative languages, experiments are carried out in words, morphemes, characters, jam, and Jamo with POS tag-level. And we analyze the distribution of the results for each sentence of the speakers to predict their age groups and to check the degree of language development. The proposed model shows an average accuracy of about 74.6 %.

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

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.