Facial expressions are a very important aspect of a theatrical performance and analysis of expressions used in histrionics is an unexplored application. The systems for facial expression analysis typically analyse SIX basic expressions viz. happiness, sadness, anger, surprise, fear, and disgust. The developed system proposes to analyse NINE expressions that are specific to Indian histrionics and are described in ancient literature as a combination of relevant facial feature states. In this respect, the feature state approach using sixEuclidean distance measures is designed to ensure the individual contributions of facial components during classification. To make the system invariant to the original facial features and to invalidate the effect of the same, a subject-dependent approach is used in this study. This approach compares neutral and expressive images of the same subject based on distance measures to determine the feature state. The process of expression recognition using facial component states is modelled and simulated using a Coloured Petri Net (CPN) tool. A novel state model is designed for the identification of expressions using a minimum number of states. A database that includes facial images exhibiting nine performance-specific expressions is created. Upon testing, the usereither receives information about correct expressions or about incorrectly positioned facial states which then can be used for improvisation. This way, the proposed system acts as an e-tutor and helps the performers for the betterment of their expression skills irrespective of the classification result. The proposed system is tested on 1440 samples from the dataset. For 1190 samples, all the feature states are found to be correct, and the image is labelled with an expression. For the rest 250 samples, the e-tutor system provided information about the incorrect feature states. The person-dependent method allows the system to be used for global users irrespective of their age, gender, and ethnicity.