Introduction: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifi er is proposed for face recognition: the novelty classifi er. Methods: The performance of a novelty classifi er is compared with the performance of the nearest neighbor classifi er. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. Results: In identifi cation mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verifi cation mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identifi cation mode is equal to 98.5%, and in the verifi cation mode, 88%. Conclusion: For face recognition, the novelty classifi er performs comparable to the best results already published in the literature, which further confi rms the novelty classifi er as an important pattern recognition method in biometry.