STUDY ON FEATURE EXTRACTION OF PIG FACE BASED ON PRINCIPAL COMPONENT ANALYSIS

Individual identification and behavioural analysis of pigs is a key link in the intelligent management of a piggery, for which the computer vision technology based on application and improvement of deep learning model has become the mainstream. However, the operation of the model has high requirements to hardware, also the model is of weak interpretability, which makes it difficult to adapt to both the mobile terminals and the embedded applications. In this study, it is first put forward that the key facial features of pigs can be extracted by Principal Component Analysis method first before the eigenface method is adopted for verification tests to reach an average accuracy rate of 74.4%; the key features, for which the most identifiable ones are in turn, respectively, face contour, nose, ears and other parts of the pigs, can be visualized, and this is different from the identification features adopted in manual identification. This method not only reduces the computational complexity but is also of strong interpretability, so it is suitable for both the mobile terminals and the embedded applications. In some way, this study provides a systematic and stable guidance for livestock and poultry production.