Simple SummaryLivestock face recognition has become one of the research hotspots; animal face recognition refers to identification recognition based on livestock face images. Face normalization is an important step in face recognition, which refers to extracting animal facial images from raw images and aligning them through methods such as rotation. However, it appears that no previous studies have focused on livestock face normalization. To address this gap in research, a new approach has been developed called the lightweight angle detection and region-based convolutional network (LAD-RCNN). LAD-RCNN is designed to detect livestock faces and their rotation angles with arbitrary directions in one stage. With the use of LAD-RCNN, livestock face normalization can be easily achieved through techniques such as clipping, rotation, and scaling based on the detected face location and rotation angle. Overall, LAD-RCNN offers promise for improving the accuracy and efficiency of livestock face recognition.AbstractWith the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in the area of animal face detection and face identification was conducted. However, there are no specialized studies on livestock face normalization, which may significantly reduce the performance of face identification. The keypoint detection technology, which has been widely applied in human face normalization, is not suitable for animal face normalization due to the arbitrary directions of animal face images captured from uncooperative animals. It is necessary to develop a livestock face normalization method that can handle arbitrary face directions. In this study, a lightweight angle detection and region-based convolutional network (LAD-RCNN) was developed, which contains a new rotation angle coding method that can detect the rotation angle and the location of the animal’s face in one stage. LAD-RCNN also includes a series of image enhancement methods to improve its performance. LAD-RCNN has been evaluated on multiple datasets, including a goat dataset and infrared images of goats. Evaluation results show that the average precision of face detection was more than 97%, and the deviations between the detected rotation angle and the ground-truth rotation angle were less than 6.42° on all the test datasets. LAD-RCNN runs very fast and only takes 13.7 ms to process a picture on a single RTX 2080Ti GPU. This shows that LAD-RCNN has an excellent performance in livestock face recognition and direction detection, and therefore it is very suitable for livestock face detection and normalization.