1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2. Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center, Zhenjiang, Jiangsu 212013, China
Abstract：To solve the problem that animals were difficult to adapt to ear tags in the breeding industry, the pig face recognition algorithm was proposed based on weighted sparse lowrank component coding by the pig face recognition in nonintrusive recognition method. The Retinex theory and the regional covariance filter were applied to estimate the illumination, and the proposed adaptive gamma correction method was used to enhance the reflection components to reduce the impact of illumination on recognition results. The lowrank components in the training samples were used to construct the dictionary matrix, and the residual function was reconstructed to process the errors and improve the recognition performance of the algorithm in dealing with the images containing dirt. The recognition rate and the timeconsuming situation were calculated based on the light and facial dirt verification experiments on the JDD2017 pig face dataset. The results show that the proposed algorithm is significantly better than the traditional sparse representation method, and it has the advantages of tolerance to illumination changes and dirt with short training time.
