An Analytical Study Of Facial Expression Recognition Using Convolutional Neural Networks For Enhanced Classification Accuracy
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Abstract
Facial Expression Recognition (FER) has become an important field in artificial intelligence and computer vision because of its various applications in intelligent systems, security, healthcare and human–computer interaction (HCI). A quantitative analytical approach was used with facial image datasets which had emotional categories such as Happy, Sad, Angry, Surprise, Fear, Disgust and Neutral. The study comprised steps such as image preprocessing, developing CNN model, evaluating the model and implementation of optimization techniques. Results showed that the proposed CNN model gives maximum accuracy while training (97.2%), while validation (95.8%) and testing (95.1%) accuracy were the highest, which means that it has a better recognition ability. The results from the expression-wise analysis revealed that highest accuracy of recognition was obtained for Happy and Surprise expression while comparatively lower recognition rate was obtained in Fear and Disgust. The results also showed that feature extraction methods, data quality, and optimization techniques like transfer learning and data augmentation greatly improved the classification results. The results of the study show that the optimized CNN based approaches can be used to recognize facial expression with better classification accuracy with an efficient and reliable solution.
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