Published Paper Details:

Automatic gender classification and age detection may be a fundamental task in computer vision which has recently attracted immense attention. Here, a new type of intelligent system which is based on several neural networks connected on two levels of classification with an efficient and simple Convolutional Neural Network (CNN) model architecture by considering gender and age estimation as a multi-label classification problem. The proposed model is trained and then evaluated on the publicly available audience benchmark dataset. The model first performs feature extraction on the input image which can classify eyes, lips, beard, hair etc supporting these featured the model will classify the gender as male or female. Here we have used the Haar Cascade for feature extraction purpose. Age is estimated with the assistance of Caffe Model. We have used various detection methods for analysis like skin colour Segmentation and lip detection and many more. The CNN model is built by using VGG-16 architecture. The CNN model is then trained using epochs, where each epoch contains a certain number of training images. To remove distorted and unwanted images, the loss Gauss function is used. For testing, the input image is given by the user. The model makes the predictions to estimate age, gender and emotion of that input image by comparing with the trained images.