Non-negative matrix factorization (NMF) has non-negative and local characteristics, and it is a new feature extraction method. NMF is an unsupervised learning method, and does not consider class information of samples applied to extract palmprint features, so the classification effect is not ideal. In order to fuse class information when the features of images are extracted, a palmprint recognition method based on non-negative matrix factorization and general discriminant analysis (GDA) is proposed. Before extracting features, the three-level wavelet transform is utilized to palmprint images to get the low-frequency sub-images. Then NMF and GDA are applied to extract palmprint features. The cosine distance between two feature vectors is calculated to match palmprint. The new algorithm is tested in PolyU plmprint database. The results show that compared with principal component analysis (PCA), independent component analysis (ICA) and NMF, the equal error rate (EER) of the new algorithm is the lowest as 0.16%, and the total time for feature extraction and matching is 0.812 s, so it meets the real-time system specification.