Identification of Gender and Age using Classification and Convolutional Networks

Today’s technology advances rapidly. A person’s face is extremely important in helping to recognise them specifically. When seen from a human viewpoint, it is fairly simple to determine a person’s gender and age just on visuals, but when viewed from a computer perspective, it is not possible. The main goal of this project is to create an algorithm that accurately predicts a person’s age and gender. HAAR cascade is one of the most used methods. Using HAAR Cascade, create a model for this project that can determine a person’s gender. Different male and female photos that were both positive and negative were used to train the model’s classifier. Various face traits are taken off. The input image’s gender will be determined by the HAAR Cascade classifier with its help. Deep Convolution neural network is used in it. It functions well even with little data. Use the Caffe deep learning framework for the age estimation challenge. Caffe offers extendable code and expressive architecture. Caffe can handle more than 60M images per day. It is one of the quickest convent implementations available as a result.