A Parallel Attention Network for Cattle Face Recognition
Jiayu Li Xuechao Zou Shiying Wang Ben Chen Junliang Xing Pin Tao
Cattle face recognition holds paramount significance in domains such as
animal husbandry and behavioral research. Despite significant progress in
confined environments, applying these accomplishments in wild settings remains
challenging. Thus, we create the first large-scale cattle face recognition
dataset, ICRWE, for wild environments. It encompasses 483 cattle and 9,816
high-resolution image samples. Each sample undergoes annotation for face
features, light conditions, and face orientation. Furthermore, we introduce a
novel parallel attention network, PANet. Comprising several cascaded
Transformer modules, each module incorporates two parallel Position Attention
Modules (PAM) and Feature Mapping Modules (FMM). PAM focuses on local and
global features at each image position through parallel channel attention, and
FMM captures intricate feature patterns through non-linear mappings.
Experimental results indicate that PANet achieves a recognition accuracy of
88.03% on the ICRWE dataset, establishing itself as the current
state-of-the-art approach. The source code is available in the supplementary
materials.