In this paper, we propose an ensemble-based approach to boost performance of Tied Factor Analysis(TFA) to over- come some of the challenges in face recognition across large pose variations. We use Adaboost.m1 to boost TFA which has shown to possess state-of-the-art face recogni- tion performance under large pose variations. To this end, we have employed boosting as a discriminative training in the TFA as a generative model. In this model, TFA is used as a base classi ̄er for the boosting algorithm and a weighted likelihood model for TFA is proposed to adjust the impor- tance of each training data. Moreover, a modi ̄ed weight- ing and a diversity criterion are used to generate more di- verse classi ̄ers in the boosting process. Experimental re- sults on the FERET data set demonstrated the improved performance of the Boosted Tied Factor Analysis(BTFA) in comparison with TFA for lower dimensions when a holistic approach is being used.