Automatic age estimation from facial images is an exciting machine learning topic that has attracted researchers’ attention over the past several years. Numerous human–computer interaction applications, such as targeted marketing, content access control, or soft-biometrics systems, employ age estimation models to carry out secondary tasks such as user filtering or identification. Despite the vast array of applications that could benefit from automatic age estimation, building an automatic age estimation system comes with issues such as data disparity, the unique ageing pattern of each individual, and facial photo quality. This paper provides a survey on the standard methods of building automatic age estimation models, the benchmark datasets for building these models, and some of the latest proposed pieces of literature that introduce new age estimation methods. Finally, we present and discuss the standard evaluation metrics used to assess age estimation models. In addition to the survey, we discuss the identified gaps in the reviewed literature and present recommendations for future research.
Facial age is one of the prominent features needed to make decisions, such as accessing certain areas or resources, targeted advertising, or more straightforward decisions such as addressing one another. In machine learning, facial age estimation is a typical facial analysis subtask in which a model learns the different facial ageing features from several facial images. Despite several studies confirming a relationship between age and gender, very few studies explored the idea of introducing a gender-based system that consists of two separate models, each trained on a specific gender group. This study attempts to bridge this gap by introducing an age estimation system that consists of two main components. The first component is a custom-built gender classifier that distinguishes females and males apart. The second is an age estimation module that consists of two models. Model A is trained only on female images, while model B is trained only on male images. The system takes an input image, extracts the facial gender then passes the image to the appropriate model based on the predicted gender label. Our age estimation models are based on the Visual Geometry Group (VGG16) networks and have been modified to fit the nature of our problem. The models produce accuracies of more than 85% individually, and the system achieves an overall accuracy of 80%. The proposed system is trained and tested on the UTK-Face dataset and cross-validated on the FG-NET dataset to validate the performance on unseen data.
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.
Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.
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