Aim: To implement the dynamic model for recognition and classification of human expression using Novel Random Forest compared over Convolution Neural Network with improved accuracy. Materials and Methods: This study contains 2 groups i.e Novel Random Forest (RF) and Convolutional Neural Network (CNN). Each group consists of a sample size of 10 and the study parameters include alpha value 0.05, beta value 0.2, and power value 0.8. Results: The Novel Random Forest is 77.9% more accurate than the Convolutional Neural Network of 75.8% in classifying the facial expressions of humans with p=0.8. Conclusion: The Novel Random Forest model is significantly better than the Convolution Neural Network in identifying human expressions.