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 non-greedy solution.This algorithm not only ensure the ability of image classification,but also decrease the average reconstruction error in image reconstruction.When applied to underwater biometric image recognition,the proposed 2DPCA-F-L1 suppresses the noise interference in underwater optical images.Experiments show that the 2DPCA-F-L1 algorithm can accurately recognize the species of underwater creatures,and has better robustness than other principal component analysis (PCA) algorithms in image reconstruction experiments.