Recently, palmprint recognition has made huge progress and attracted the attention of more and more researchers. However, current research rarely involves open-set palmprint recognition. We proposed deep ensemble hashing (DEH) for open-set palmprint recognition. Based on the online gradient boosting model, we trained multiple learners in DEH, which focus on identifying different samples. In order to increase the diversity between learners, activation loss and adversarial loss were introduced. Through minimizing activation loss, the neurons of different learners restrained each other, and through adversarial loss, the optimal distance between the features extracted by different learners was obtained. Palmprint identification and verification experiments were performed on PolyU multispectral database and our self-built databases. The results show the effectiveness of DEH in deal with open-set palmprint recognition. Compared to baseline models, DEH increased the recognition accuracy by up to 6.67% and reduced the equal error rate by up to 3.48%.