Multi-biometrics Using Facial Appearance, Shape and Temperature

We present results of the first study to examine individual and multi-modal face recognition using 2D, 3D and infra-red images of the same set of subjects. Each sensor cap-tures different aspects of human facial features; appearance in intensity representing surface reflectance from a light source, shape data representing depth values from the cam-era, and the pattern of heat emitted, respectively. We em-ploy a database containing a gallery set of 127 images and an accumulated time-lapse probe set of 297 images. Using a PCA-based approach tuned separately for 2D, 3D and IR, we find rank-one recognition rates of 90.6 % for 2D, 91.9% for 3D and 71.0 % for IR. Combining each pair of modal-ities, we find a multi-modal rank-one recognition rate of 98.7 % for 2D-3D, 96.6 % for 2D-IR and 98.0 % for 3D-IR. When all three modalities are combined, we obtain 100% recognition. The results shown in this study appear to sup-port the conclusion that the path to higher accuracy and robustness in biometrics involves use of multiple biometrics rather than the best possible sensor and algorithm for a sin-gle biometric. 1