We present an algorithm for the automatic recognition of facial features for color images of either frontal or rotated human faces. The algorithm first identifies the sub-images containing each feature, afterwards, it processes them separately to extract the characteristic fiducial points. Then Calculate the Euclidean distances between the center of gravity coordinate and the annotated fiducial points' coordinates of the face image. A system that performs these operations accurately and in real time would form a big step in achieving a human-like interaction between man and machine. This paper surveys the past work in solving these problems. The features are looked for in down-sampled images, the fiducial points are identified in the high resolution ones. Experiments indicate that our proposed method can obtain good classification accuracy.