Age and gender information are very important for various real-world applications, such as social understanding, biometrics, identity verification, video surveillance, human-computer interaction, electronic customer, crowd behaviour analysis, on-line advertisement, item recommendation. To build gender and age detector that can approximately guess the gender and age of the person (face) In a picture using Deep Learning on the Audience dataset with the help of Open CV-Pyth OpenCV Is short for Open-Source Computer Vision. Intuitively by the name, it is an open-source Computer Vision and Machine Learning library. This library is capable of processing real-time Image and video while also boasting analytical capabilities. It supports the Deep Learning frameworks TensorFlow, Caffe, and PyTorch. OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross platform. In this Python Project, we will use Deep Learning to accurately Identify the gender and age of a person from a single Image of a face. We will use the models trained by Tal Hassner and GIl Levi. The predicted gender may be one of 'Male' and 'Female', and the predicted age may be one of the following ranges- [(1-2), (3-5), (6-14), (16-22), (25-30), (32-40), (45-50), (>60)] (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, we make this a classification problem instead of making it one of regression.