A 3D algorithm for unsupervised face identification

Abstract

With the increasing availability of low-cost 3D data acquisition devices, the use of 3D face data for the recognition of individuals is becoming more appealing and computationally feasible. This paper proposes a completely automatic algorithm for face registration and matching. The algorithm is based on the extraction of stable 3D facial features characterizing the face and the subsequent construction of a signature manifold. The facial features are extracted by performing a continuous-to-discrete scale-space analysis. Registration is driven from the matching of triplets of feature points and the registration error is computed as shape matching score. Conversely to most techniques in the literature, a major advantage of the proposed method is that no data pre-processing is required. Therefore all presented results have been obtained exclusively from the raw data available from the 3D acquisition device. The method has been tested on the Bosphorus 3D face database and the performances compared to the ICP baseline algorithm. Even in presence of noise in the data, the algorithm proved to be very robust and reported identification performances which are aligned to the current state of the art, but without requiring any pre-processing of the raw data.