Exploring Attributions in Convolutional Neural Networks for Cow Identification

Face recognition and identification is a method that is well established in traffic monitoring, security, human biodata analysis, etc. Regarding the current development and implementation of digitalization in all spheres of public life, new approaches are being sought to use the opportunities of high technology advancements in animal husbandry to enhance the sector’s sustainability. Using machine learning the present study aims to investigate the possibilities for the creation of a model for visual face recognition of farm animals (cows) that could be used in future applications to manage health, welfare, and productivity of the animals at the herd and individual levels in real-time. This study provides preliminary results from an ongoing research project, which employs attribution methods to identify which parts of a facial image contribute most to cow identification using a triplet loss network. A new dataset for identifying cows in farm environments was therefore created by taking digital images of cows at animal holdings with intensive breeding systems. After normalizing the images, they were subsequently segmented into cow and background regions. Several methods were then explored for analyzing attributions and examine whether the cow or background regions have a greater influence on the network’s performance and identifying the animal.