Age and gender prediction using Haar cascade algorithm and fine-tuned CNN framework

Age and gender prediction using unfiltered facial images is an essential task these days. We apply deep learning algorithm convolutional neural network (CNN) with multiple datasets such as UTKFace, Adience, and two custom datasets all combined for best performance. When we glanced over the existing works, we found that the existing models are efficient regarding age prediction, another problem was the computational power requirement, which is less useful in real-time and without a GPU. In this research, we used fewer layers to make this model usable in real-time and eliminate the requirement of GPUs for computational power. We employed the Haar cascade algorithm for face detection, and CNN with 12 layers for the other two modules, age prediction, and gender prediction. Combining different datasets and training each module on different balanced and unbiased datasets, we have achieved a higher accuracy regarding age prediction.