We propose an algorithm for extracting facial features robustly from images for face recognition under large pose variation. Rectangular facial features are retrieved via the by-products of an embedded Hidden Markov Model (HMM) which decodes an observed face image into a state sequence. While an HMM is able to segment images into features at a fixed pose, multiple HMMs are trained for each individual to robustly extract features under large pose variation. Using the extracted features of each individual, appearance models based on subspaces are constructed for face identification and verification. The effectiveness of the proposed approach is validated through empirical studies against numerous methods using the CMU PIE database. Our experiments demonstrate that the proposed approach is able to extract facial features robustly, thereby rendering superior results in identification and superior performance in verification under large pose variation.