3D Facial Similarity Measurement and Its Application in Facial Organization

We propose a novel framework for 3D facial similarity measurement and its application in facial organization. The construction of the framework is based on Kendall shape space theory. Kendall shape space is a quotient space that is constructed by shape features. In Kendall shape space, the shape features can be measured and is robust to similarity transformations. In our framework, a 3D face is represented by the facial feature landmarks model (FFLM), which can be regarded as the facial shape features. We utilize the geodesic in Kendall shape space to represent the FFLM similarity measurement, which can be regarded as the 3D facial similarity measurement. The FFLM similarity measurement is robust to facial expressions, head poses, and partial facial data. In our experiments, we compute the distance between different FFLMs in two public facial databases: FRGC2.0 and BosphorusDB. On average, we achieve a rank-one facial recognition rate of 98%. Based on the similarity results, we propose a method to construct the facial organization. The facial organization is a hierarchical structure that is achieved from the facial clustering by FFLM similarity measurement. Based on the facial organization, the performance of face searching in a large facial database can be improved obviously (about 400% improvement in experiments).