An Integrated CNN-BiLSTM Approach for Facial Expressions

Deep learning algorithms have demonstrated good performance in many sectors and applications. Facial expression recognition (FER) is recognizing the emotions through images. FER is an integral part of many applications. With the help of the CNN-BiLSTM integrated approach, higher accuracy can be achieved in identification of the facial expressions. Convolutional neural networks (CNN) consist of a Conv2D layer, dividing the given images into batches, performing normalization and if required flattening the data i.e. converting the data in a 1D array and achieving a higher accuracy. BiLSTM works on two LSTMs i.e. one in the forward direction and the other in a backward direction. One can use LSTM to process the images (datasets) however, it is suggested with the help of BiLSTM can predict the expressions with more accuracy. Input data is available in both the direction (forward and backward) which helps maintaining the context. Using LSTM CNN and BiLSTM always helps increasing the prediction accuracy. Application areas where a BiLSTM can give more prediction accuracy are the forecasting models, text recognition, speech recognition, classifying the large data and the proposed facial expression recognition. The integrated approach (CNN and BiLSTM) increases the accuracy significantly as discussed in the results and discussion section. This approach could be categorized as a fusion technique where two methods (approaches) are integrated to get higher accuracy. The results and discussion section elaborates the effectiveness of the integrated approach compared to HERO: human emotions recognition for realizing the intelligent internet of things. As compared to the HERO approach CNN-BiLSTM gives good results in terms of precision and recall.

@article{Pansambal2024,
  title     = {An Integrated CNN-BiLSTM Approach for Facial Expressions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {3},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {B. H. Pansambal and A. B. Nandgaokar and J. L. Rajput and Abhay Wagh},
  doi       = {10.14569/IJACSA.2024.0150398},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150398}
}