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This paper presents a fully automated 3D face authentication (verification) and recognition (identification) method and recent results from our work in this area. The major contributions of our paper are: (a) the method can handle data with different facial expressions including hair, upper body, clothing, etc. and (b) development of weighted features for discrimination. The input to our system is a triangular mesh and it outputs a matching % against a gallery. Our method includes both surface and curve based features that are automatically extracted from a given face data. The test set for authentication consisted of 117 different people with 421 scans including different facial expressions. Our study shows equal error rate (EER) at 0.065% for normal faces and 1.13% in faces with expressions. We report verification rates of 100% in normal faces and 93.12% in faces with expressions at 0.1% FAR. For identification, our experiment shows 100% rate in normal faces and 95.6% in faces with expressions. From our experiment we conclude that combining feature points, profile curve, and partial face surface matching gives better authentication and recognition rate than any single matching method.