  With the demand for standardized large-scale livestock farming and the
development of artificial intelligence technology, a lot of research in area of
animal face recognition were carried on pigs, cattle, sheep and other
livestock. Face recognition consists of three sub-task: face detection, face
normalizing and face identification. Most of animal face recognition study
focuses on face detection and face identification. Animals are often
uncooperative when taking photos, so the collected animal face images are often
in arbitrary directions. The use of non-standard images may significantly
reduce the performance of face recognition system. However, there is no study
on normalizing of the animal face image with arbitrary directions. In this
study, we developed a light-weight angle detection and region-based
convolutional network (LAD-RCNN) containing a new rotation angle coding method
that can detect the rotation angle and the location of animal face in
one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a
single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset
including goat dataset and gaot infrared image. Evaluation result show that the
AP of face detection was more than 95% and the deviation between the detected
rotation angle and the ground-truth rotation angle were less than 0.036 (i.e.
6.48{\deg}) on all the test dataset. This shows that LAD-RCNN has excellent
performance on livestock face and its direction detection, and therefore it is
very suitable for livestock face detection and Normalizing. Code is available
at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/
