Age Group Classification using Convolutional Neural Network (CNN)

Abstract

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.

Twofold Face Detection Approach in Gender Classification Using Deep Learning

“…The comparison result with related research is shown in Table 3. Furthermore, the results of this study have been compared to previous research [27] using the same model but using a different dataset of All-Age Face (AAF) datasets in different amounts of data. In addition, there are also similarities in terms of data splitting methods that use the hold out method but with different amounts on each section.…”

“…In addition, there are also similarities in terms of data splitting methods that use the hold out method but with different amounts on each section. Mustapha et al [27] proposed an age group classification to address the issue of class imbalance in CNN-based classifications. Although there is a large difference in the number of datasets, the performance in terms of accuracy varies only marginally.…”

“…The proposed experiment is evaluated using a confusion matrix that will obtain the results of accuracy, precision, recall and F-1 scores [27]. Figure 9 provides an example of a confusion matrix.…”

Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

“…Assessments were made on sketch classification performance and gender verification accuracy based on how many images were taken from each individual. Demographic variables such as categorization, age, ethnicity, and gender have a significant impact on the appearance of the human face, with each category further subdivided into classes such as black and white, male and female, young (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), middle age (30-50), and old (50-70). Most students look more like their peers in their age group than they do those in other age groups.…”

“…Race recognition (RR), which has numerous applications in surveillance systems, image and video interpretation, analysis, and others, is a challenging problem to solve. The use of a deep-learning model to help solve that challenge has been analyzed in [28,29]. A race recognition framework (RRF) was proposed, which included an information collector (IC), face detection and preprocessing (FD&P), and RR modules.…”