Using Convolutional Neural Networks (CNN) for Age and Gender Prediction

Abstract

The network, security, and care have all become more dependent on age and gender identification. It's commonly used for children's access to age-appropriate content. To expand its reach, social media uses it to provide layered adverts and marketing. Face recognition has progressed to the point where we need to map it out further in order to achieve more usable results using various methodologies. In this study, we suggest using deep CNN to improve age and gender prediction. We show that considerable improvements may be found in a variety of applications, such as face recognition. Due to its vast applications in many facial analysis challenges, automatic age and gender prediction from face photos have received a lot of interest recently. Using the Caffe Model Architecture of Deep Learning Framework, we were able to considerably enhance age and gender recognition by learning representations using deep-convolutional neural networks (CNN). We propose a simplified convolution net design that may be employed even when learning data is scarce. In light of recent events, We demonstrate that our method greatly outperforms current state-of-the-art methods for age and gender estimation. This paper covers predicting age and gender, as well as face detection and recognition using atrained model. In internal evaluation, the Caffe framework outperforms Tensor Flow by 1 to 5 times. Following the training phase, we'll utilize the. Caffe model was taught to make predictions based on new data that had never been seen before. We'll write a Python script that uses Open CV for the project code. The trained model will detect the person's face and correctly forecast their age and gender.