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For interpersonal relations among humans, facial expressions are extremely important. Due to the complications in collecting required features from the frequently changing surroundings, uneven reflection from light sources, and many other aspects, facial expression recognition will encounter significant problems. A novel facial image recognition approach is proposed in this paper. Initially, a face image enhancement framework is created that is capable of enhancing the features of a face in a complicated context for this strategy. The improved Alexnet neural network is then created, which is based on the Alexnet architecture. Multi-scale convolution process is utilised in the improved Alexnet to enhance feature extraction capability. Batch normalisation is used for preventing network overfitting while also improving the model’s robustness. The Adabound optimizer and the Relu activation function are used to improve convergence and accuracy. The facial image feature improvement approach is helpful to increasing the capability of the improved Alexnet in trials from many aspects. For face images acquired in the natural surroundings, our technique displays significant stability, serving as a benchmark for the intelligent prediction of other facial images.


Facial Expression Recognition, Deep Learning, Convolutional Neural Network, Improved Alexnet
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