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ABSTRACT:
Since the advent of social media, there has been an increased interest in automatic age and gender classification through facial images. So, the process of age and gender classification is a crucial stage for many applications such as face verification, aging analysis, ad targeting and targeting of interest groups. Yet most age and gender classification systems still have some problems in real-world applications. This work involves an approach to age and gender classification using multiple convolutional neural networks (CNN). The proposed method has 5 phases as follows: face detection, remove background, face alignment, multiple CNN and voting systems. The multiple CNN model consists of three different CNN in structure and depth; the goal of this difference It is to extract various features for each network. Each network is trained separately on the AGFW dataset, and then we use the Voting system to combine predictions to get the result.
INTRODUCTION
Biometrics, is the science of analyzing the physical or behavioral characteristics of each individual that enable the authentication of their identity in a reliable manner, it offers significant advantages conventional identification methods, such as passwords and cards, are not transferable, exclusive to each person and are not lost or stolen, particularly because of biometric features. The range of biometric solutions relies on user approval, security, cost and time for implementation…etc. Recently, face recognition has been one of the most interesting tasks in pattern recognition, many applications use this technique because the human face is considered a very rich source of information. In particular, gender and age are facial features that can be very useful for a multitude of applications, for example an automatic gender and age prediction system is used to profile customers who are interested for a product or for target advertising. The areas of age and gender classification have been studied for decades. Until detailing the methods used in this article, we will first provide a summary of the facial recognition experiments carried out by scholars, which can be grouped into tree classes of interest. Over the last decade, the rate of image uploads to the Internet has grown at a nearly exponential rate. This newfound wealth of data has empowered computer scientists to tackle problems in computer vision that were previously either irrelevant or intractable. Consequently, we have witnessed the dawn of highly accurate and efficient facial detection frameworks that leverage convolutional neural networks under the hood. One of the most critical barriers that face any system to age estimation or age-classification is the absence of a consistent pattern of facial aging. This is due to the nature of human faces, and the stages of aging may differ from one human to another.
