Facial Recognition of Cattle Based on SK-ResNet

Abstract

With the gradual increase of the scale of the breeding industry in recent years, the intelligence level of livestock breeding is also improving. Intelligent breeding is of great significance to the identification of livestock individuals. In this paper, the cattle face images are obtained from different angles to generate the cow face dataset, and a cow face recognition model based on SK_ResNet is proposed. Based on ResNet-50 and using a different number of sk_Bottleneck, this model integrates multiple receptive fields of information to extract facial features at multiple scales. The shortcut connection part connects to the maximum pooling layer to reduce information loss; the ELU activation function is used to reduce the vanishing gradient, prevent overfitting, accelerate the convergence speed, and improve the generalization ability of the model. The constructed bovine face dataset was used to train the SK-ResNet-based bovine face recognition model, and the accuracy rate was 98.42%. The method was tested on the public dataset and the self-built dataset. The accuracy rate of the model was 98.57% on the self-built pig face dataset and the public sheep face dataset. The accuracy rate was 97.02%. The experimental results verify the superiority of this method in practical application, which is helpful for the practical application of animal facial recognition technology in livestock.