This paper proposed five new types of facial features for face recognition. Ada-boost is used to detect face firstly. False detected faces are removed by dynamic background modeling and skin color detection. Skewed face is also calibrated to achieve higher accuracy. Based on Active Shape Modeling, the five new types of facial features including gradient histograms of facial components, vertical/horizontal projection of facial edge points, signature of facial components, multiple vertical/horizontal line segments within facial shape, and face template could be extracted. According to the classification capability, features are associated with different weights while during matching. Nearest neighbor classifier is deployed for face recognition by using the averaged of feature points of a person as the center. The size of database is 200 people which are selected from the face databases of MIT and ESSEX. Five images per person were used for training and 491 images were tested. The recognition rate was 98.3% and the processing speed reached 220ms per frame on a general personal computer.