Facial based human age estimation has attracted lot of attention nowadays. Age estimation has become quite challenging task due to variation in lighting conditions, poses, and facial expression. Despite so much research in facial based human age estimation still there is room to improve performance. To improve accuracy we present age estimation using deep belief network. Deep belief network have shown superior performance as compared to other classification models. Success of deep belief network lies in contrastive divergence algorithm. Facial images passes though viola johns facial detection algorithm, once face is detected facial featured are extracted using active appearance and scattering transform feature method. These feature extraction model not only extracts geometric features but also extracts texture features. Subsequently deep belief network classification model is built on partitioned training images and evaluated on testing images. We performed experimentation on training images. Dataset and results are obtained by varying training percentages. Compared to other age estimation models we achieved low mean absolute error of 4.95 for 70% training images dataset. This study shows that due to inclusion of deep belief network performance is excelled. © 2020, Springer Nature Switzerland AG.