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Understanding a person’s age and gender just by looking at their face has become an important area of research, with many real-world uses. In this project, we used the Caffe deep learning framework to improve how accurately we can analyse age and gender from face images. By training deep convolutional neural networks (CNNs), we were able to get much better results than existing methods. We also designed a simpler version of the network that still works well, even when there's little or no training data available. Overall, our method offers a more effective and efficient way to perform age and gender analysis and sets a strong standard for future work in this field.