Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

Facial expression recognition (FER) remains a hot research area among computer vision researchers and still becomes a challenge because of high intra-class variations. Conventional techniques for this problem depend on hand-crafted features, namely, LBP, SIFT, and HOG, along with that a classifier trained on a database of videos or images. Many execute perform well on image datasets captured in a controlled condition; however not perform well in the more challenging dataset, which has partial faces and image variation. Recently, many studies presented an endwise structure for facial expression recognition by utilizing DL methods. Therefore, this study develops an earthworm optimization with an improved SqueezeNet-based FER (EWOISN-FER) model. The presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization (CLAHE) technique as a pre-processing step. In addition, the improved SqueezeNet model is exploited to derive an optimal set of feature vectors, and the hyperparameter tuning process is performed by the stochastic gradient boosting (SGB) model. Finally, EWO with sparse autoencoder (SAE) is employed for the FER process, and the EWO algorithm appropriately chooses the SAE parameters. A wide-ranging experimental analysis is carried out to examine the performance of the proposed model. The experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.