Humans use their faces to express their emotions and intentions in a simple and natural way. Face expressions are the essential components of nonverbal communication. In human-computer interaction and affective computing, facial expression recognition has various applications. There are several methods devised for recognition and classification of facial expression; still, the accurate recognition is the challenging task. Hence, in this research an automatic facial expression recognition and classification based on deep learning is introduced. Initially the input image is collected from facial expression recognition dataset. Then, the collected image is fed into pre-processing using Gaussian filtering which is used for noise reduction. Then the pre-processed images are given to feature extraction phase using Gray-Level Co-Occurrence Matrix (GLCM). GLCM is used to extract texture features for the facial expression recognition. Then the EfficientNet-B7 is utilized for the recognition and classification of facial expression due to the enhanced outcome with faster inference and smaller size. The proposed IC_EfficientNet method combines the gannet’s ability to capture food with the coot bird’s ability to forage. The optimization technique achieves higher convergence rates due to this hybridization, which improves the EfficientNet-B7 model's parameter tuning. In comparison to existing techniques, the hybrid Improved Coot (IC) algorithm balances exploration and exploitation, leading to a quicker and more effective optimization process. The proposed IC_EfficientNet provides better results compared to existing methods such as Deep Neural Network (DNN), MultiLayer Perceptron (MLP) neural network, Facial Detection using a Convolutional Neural Network (FD-CNN) and Convolutional Neural Network (CNN). Thus, the proposed IC_EfficientNet provides the better outcome in terms of Accuracy, Specificity, Precision, Recall, F1-Measure, and MSE acquired the better outcome of 99.13, 98.80, 97.80, 99.13, 98.44, and 0.87 respectively.

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