Goat Face Recognition Model Based on Wavelet Transform and Convolutional Neural Networks

To recognize an individual goat under farm conditions, a novel goat face recognition model named DWT - GoatNet was proposed based on wavelet transform and convolutional neural networks, which integrated frequency domain features and spatial domain features. Firstly, facial images of a total of 30 highly similar Xinong Saanen dairy goats were collected under two different light conditions, daytime and night. Some images were discarded based on structural similarity ( SSIM ) , and the remaining images were cropped manually. Image sets were also augmented by operations of blur, brightness adjustment, translation, rotation, noise addition and scaling. Secondly, a goat face feature extraction module was designed based on two-dimensional discrete wavelet transform (2D -DWT) and convolution operation to achieve feature fusion. Then, with this module, a classification module was added and a convolutional neural network named DWT - GoatNet was built. Finally, the combination of hyper-parameters was optimized and goat face recognition model was formed. The experimental results showed that the accuracy of the proposed goat face recognition model can reach 99. 74% and 99. 89% , respectively, on test set under different light conditions of daytime and night, which was higher than that of some classical CNNs such as AlexNet, VGGNet-16, GoogLeNet, ResNet-50 and DenseNet-121 , while the DWT - GoatNet can provide an effective recognition for some related fields of precision farming and agricultural insurances. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.