F-norm is sensitive to outlier data,while L1-norm can significantly reduce the sensitivity and cannot effectively control reconstruction errors.To tackle the problem,we take both F-norm and L1-norm as the distance metric of the objective function,and propose a joint- norm two-dimensional principal component analysis (2DPCA) algorithm called 2DPCA-F-L1,and give its n on-greedy solution. This algorithm not only ensure the ability of image classification,but also dec rease the average reconstruction error in image reconstruction.When applied to underwater biometr ic image recognition,the proposed 2DPCA-F-L1 suppresses the noise interference in unde rwater optical images.Experiments show that the 2DPCA-F-L1 algorithm can accurately recogniz e the species of underwater creatures,and has better robustness than other principal component analysis (PCA) algorithms in ima ge reconstruction experiments.