THREE DIMENSIONAL FACE RECOGNITION

  Abstract:

Principal Component Analysis (PCA) of three-dimensional face models can be used to perform recognition to a high degree of accuracy. However, experimentation with two-dimensional face images has shown that PCA-based systems are improved by incorporating Linear Discriminant Analysis (LDA) approach. In this paper we introduce the fisher surface method of face recognition: An adaptation of the two-dimensional fisherface approach to three-dimensional facial surface data. Testing a variety of pre-processing techniques, we identify the most effective facial surface representation and distance metric for use in such application areas as security, surveillance and data compression. The effect of using a variety of facial surface representations and suggest a method of identifying and extracting useful qualities offered by each system.Combing these components into a unified surface subspace, we create a threedimensional face recognition system producing significantly lower error rates than individual systems tested on the same data. We evaluate systems by performing up to 1,079,715 verification operations on a large test set of 3D face models. Results are presented in the form of false acceptance and false rejection rates, taking the equal error rate as a single comparative value.

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