The authors of the Goat Images Dataset emphasize the significance of automatic individual animal identification in the context of livestock farming, with its potential to enhance breeding histories and contribute substantially to breeding and genetic management programs. In the current livestock identification practices, such as the utilization of various tags, tattoos, paint brands, and microchips, manual identification processes prevail, characterized by their time-consuming, expensive, and unreliable nature. In their research, authors present a deep learning-based solution, offering a fully automated pipeline for the detection and recognition of goats’ faces, a task more complex than human face recognition due to the high similarity among individual goats.

In the broader context of livestock farming, the automatic identification of individual animals has become a pivotal element, enabling comprehensive data collection and report generation to support farm management. While historical methods like tag identification have been employed, they pose challenges when applied to animals kept in groups, such as goats, as individual observation and data collection become arduous. To overcome this challenge, researchers are turning to deep learning techniques, which have found application in various aspects of animal farming, including biometric identification, activity monitoring, body condition scoring, and behavioral analysis.

The use of deep learning algorithms is gaining traction across the animal industry, addressing identification challenges by focusing on specific body parts, such as sheep retinas, cattle body patterns, and pig faces. However, challenges persist in obtaining clear and comprehensive datasets for these methods in real-world, uncontrolled environments. As of January 2022, the performance of CNNs for individual goat identification using facial images remains unexplored. The paper aims to extract robust features that enhance the distinguishability between individual goats in facial images. The proposed methodology comprises two primary steps: face detection and identity recognition. Given an input image, the first step involves the detection of faces and facial landmarks using a Yolo-based method, followed by cropping these regions into fixed segments. Facial landmarks serve to geometrically normalize the face. Ultimately, a custom CNN model extracts features for the detected regions and predicts classification probabilities. Notably, the approach covers not only face recognition but also automatic face detection and alignment based on the eye centroid, akin to human face recognition.

Two different types of image datasets are uploaded. The first dataset (dataset_1) is useful for detection purpose. This consists of a total 1680 goat image where the face, eye, mouth and ear bounding boxes are given in YOLO format. On the other hand, the second dataset (dataset_2) is for face recognition and facial expression analysis. In total 1311 images are captured from 10 individuals of a Chinese farm (34.2721° N, 108.0847° E).