We present results of the largest experimental investigation of 3D ear biometrics to date. ICP-based approaches are carefully explored, and the best rank one recognition rate achieves 98.8%. Only 4 cases out of 302 are incorrectly matched. This result is encouraging in that it suggests the uniqueness of the human ear and its potential applicability as a biometric.
This is the first study to compare the PCA and ICP approaches to 3D face recognition, and to propose a local region approach coping with expression variation in 3D face recognition. A new algorithm for 3D face recognition is proposed for handling expression variation. It uses a surface registration-based technique for 3D face recognition. The proposed method uses a fully automatic approach to use to initialize the 3D matching. Results are presented for gallery and probe datasets of 355 subjects imaged in 3D, with significant time lapse between gallery and probe images of a given subject yielding 3,205 3D models. We find that an ICP-based method performs better than a PCA-based method. The evaluation results show that our proposed new algorithm substantially improves performance in the case of varying facial expression. We also examined subject factors in the proposed method on 3D face models by age and gender.
This study considers face recognition using multiple imaging
modalities. Face recognition is performed using a PCA-based
algorithm on each of three individual modalities: normal 2D
intensity images, range images representing 3D shape, and infra-red
images representing the pattern of heat emission. The algorithm is
separately tuned for each modality. For each modality, the gallery
consists of one image of each of the same 127 persons, and the probe
set consists of 297 images of these subjects, acquired with one or
more week's time lapse. In this experiment, we find a rank-one
recognition rate of 71% for infra-red, 91% for 2D, 92% for 3D.
We also consider the multi-modal combination of each pair of
modalities, and find a rank-one recognition rate of 97% for 2D plus
infra-red, 98% for 3D plus infra-red, and 99% for 3D plus 2D. The
combination of all three modalities yields a rank-one recognition
rate of 100%. We conclude that multi-modal face recognition
appears to offer great potential for improved accuracy over using a
single 2D image. Larger and more challenging experiments are needed
in order to explore this potential.