The problem of tracking and recognizing faces in real-world, noisy videos is addressed. While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently. This paper describes a new method to perform face recognition from video sequences. Faces are detected, tracked and recognized in a video sequence using Hidden Markov Model and K-nearest neighbor. Feature extraction for tracking and recognition is performed by principal component analysis. This process also allows locating and extracting facial feature regions around the eyes, nose and mouth.