Sika Deer Facial Recognition Model Based on SE-ResNet

Abstract

The scale of deer breeding has gradually increased in recent years and better information management is necessary, which requires the identification of individual deer. In this paper, a deer face dataset is produced using face images obtained from different angles, and an improved residual neural network (ResNet)-based recognition model is proposed to extract the features of deer faces, which have high similarity. The model is based on ResNet-50, which reduces the depth of the model, and the network depth is only 29 layers; the model connects Squeeze-and-Excitation (SE) modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer. A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock. The Rectified Linear Unit (ReLU) activation function in the network is replaced by the Exponential Linear Unit (ELU) activation function to reduce information loss during forward propagation of the network. The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SE-Resnet, which is demonstrated to identify individuals accurately. By setting up comparative experiments under different structures, the model reduces the amount of parameters, ensures the accuracy of the model, and improves the calculation speed of the model. Using the improved method in this paper to compare with the classical model and facial recognition models of different animals, the results show that the recognition effect of this research method is the best, with an average recognition accuracy of 97.48%. The sika deer face recognition model proposed in this study is effective. The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.