Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every field, from traditional to online platforms. Additionally, ICD forms an essential part of a rich cultural and intangible heritage, which at all costs must be modernized and preserved. In this paper, we have attempted an exhaustive classification of dance forms into eight categories. For classification, we have proposed a deep convolutional neural network (DCNN) model using ResNet50, which outperforms various state-of-the-art approaches. Additionally, to our surprise, the proposed model also surpassed a few recently published works in terms of performance evaluation. The input to the proposed network is initially pre-processed using image thresholding and sampling. Next, a truncated DCNN based on ResNet50 is applied to the pre-processed samples. The proposed model gives an accuracy score of 0.911.

Recent research works have been focussing on estimating age from facial images. Age estimation from faces basically involves two sub-processes: extracting features and estimating learning function. Age classification from an input image is the task at hand in this project; age will be classified into 3 categories: 1) Toddler, 2) Teen, 3) Adult. Classifying age automatically from an image has been widely used in our day-to-day lives, particularly in the listed fields: biometrics, surveillance systems, and commercial kiosks. The purpose of this study is to categorize facial images based on their age. Prominently, previously existing research works were performed on contrived and unreal images curated in laboratories. Those images did not correctly portray the distinctions and fluctuations that are evident in real human faces. This paper uses deep convolutional neural networks (CNN) on the available data to overcome the above discussed challenge.