This paper investigates the use of facial gestures for identity recognition. This is the first time that such a quantitative evaluation is conducted, comparing the analyses of 2D versus 3D dynamic data of verbal and nonverbal facial actions. Suitable data processing and feature extraction methods are examined, then a number of pattern matching techniques including the Fr´echet distance, Correlation Coefficients, Hidden-Markov Models, Dynamic TimeWarping and its derived forms are compared, in light of which an improved algorithm is proposed. Finally, a face recognition prototype using facial dynamics is built, achieving an Equal Error Rate EER=1.6%.