Thomas Heseltine

Three-dimensional face recognition: an eigensurface approach

We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a range of image processing techniques we identify the most effective surface representation for use in such application areas as security, surveillance, data compression and archive searching.

Three-Dimensional Face Recognition: A Fishersurface Approach

Previous work has shown that 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), as with Belhumier et al’s fisherface approach. In this paper we introduce the fishersurface 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. Results are presented in the form of false acceptance and false rejection rates, taking the equal error rate as a single comparative value.

Three-dimensional face recognition using combinations of surface feature map subspace components

In this paper, we show the effect of using a variety of facial surface feature maps within the Fishersurface technique, which uses linear discriminant analysis, and suggest a method of identifying and extracting useful qualities offered by each surface feature map. Combining these multi-feature subspace components into a unified surface subspace, we create a three-dimensional face recognition system producing significantly lower error rates than individual surface feature map 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, generated by varying a decision threshold applied to a distance metric in surface space.

Three-Dimensional Face Recognition Using Surface Space Combinations.

In this paper we test a range of three-dimensional face recognition systems, based on the fishersurface method developed in previous work. We show 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, generated by varying a decision threshold applied to a distance metric in combined surface space.

Combining multiple face recognition systems using Fisher's linear discriminant

The application of image processing as a pre-processing step to methods of face recognition can significantly improve recognition accuracy. However, different image processing techniques provide different advantages, enhancing specific features or normalising certain capture conditions. We introduce a new method of isolating these useful qualities from a range of image subspaces using Fishers linear discriminant and combining them to create a more effective image subspace, utilising the advantages offered by numerous image processing techniques and ultimately reducing recognition error. Systems are evaluated by performing up to 258,840 verification operations on a large test set of images presenting typical difficulties when performing recognition. Results are presented in the form of error rate curves, showing false acceptance rate (FAR) vs. false rejection rate (FRR), generated by varying a decision threshold applied to the euclidean distance metric performed in combined face space.