Discriminant statistical analysis of local facial geometrical regions

The residences of Japanese Archipelago mainly include the three human populations; the Ainu, the Mainland Japanese and the Ryukyuan, which can be inferred by genome-wide single-nucleotide polymorphism (SNP) data and characterized by generic base sequences. In the other hand, the genetic association of human facial morphological variation recently becomes a more and more active research field, which aims to quantitatively analyze the possible relation measure between the gene base and a kind offacial morphological variations. This study attempts to explore the discriminated phenotype features of the common facial morphological variations between the Mainland Japanese and the Ryukyuan; the difference of phenotype features between these two populations is prospected to infer different gene base sequences. In order to explore the facial phenotype features, we propose a framework of local statistical analysis for adjacent geometrical regions of 3D facial images. Therein, we firstly select the surface points with higher distinguishable values based fisher linear discriminate analysis as discriminated coordinate vectors, and further cluster them into local geometrical groups for morphological analysis. The extracted local phenotype features are applied for identification of two populations, and achieve the comparable or better performances than the global phenotype feature, which manifests the possibility for association analysis between local morphological phenotype and the genes.