Cattle face generation using an adversarial network with bounding boxes of facial subregions

Abstract

Data collection in precision livestock farming is a serious challenge. The scarcity of annotated image datasets for precision livestock problems limits the potential of deep learning algorithms. The information learned by the Generative Adversarial Networks (GANs) can be employed to improve the performance of machine learning tasks, such as classication. Specic subregions of the facial cattle region, such as the muzzle, ears, and eyes, are signicant indicators of pain, discomfort, and stress in animals, as well as being used for individual identication. In this context, in the present work we develop a new architecture that enhances the spatial localization of facial interest subregions. The proposed method combines bounding box modules with a GAN known in the literature, named BigGAN. The hypothesis is that the incorporation of spatial location information, provided by interest subregions bounding boxes, such as the muzzle, ears, and eyes, can improve the generation of synthetic cattle faces images. Thus, we proposed a new conguration of these modules, based on providing multiple objects in a scene. The modules are attached at dierent depths in the generator, forming a resolution pyramid, and at a specic level in the discriminator. We also propose to use a multiscale boxes to represent the interest subregions. The premise is to smooth out the transitions between the objects and the background, while reinforcing the objects’ features. To train the model, we ltered a pre-annotated dataset for the detection task and adapted it for the generation task. The new dataset includes 9, 495 cattle faces and a subset of bounding box annotations. To assess the feasibility of the proposal, we conducted extensive analysis, both quantitative and qualitative. The experimental results showed improvements provided by the inclusion of additional information for the interest subregions via bounding box modules. The capacity to arbitrarily insert the subregions’ position allows for possible applications such as facial generation designed by user.