Sequential Clustering based Facial Feature Extraction Method for Automatic Creation of Facial Models from Orthogonal Views

TL;DR

This paper introduces a sequential clustering method for extracting facial features from orthogonal view images to automatically generate 3D facial models, improving robustness and accuracy for applications like face recognition.

Contribution

It presents heuristic algorithms for reliable feature extraction and 3D model construction from 2D orthogonal view images, addressing non-ideal conditions.

Findings

Effective feature extraction from frontal and profile views

Successful 3D model deformation based on 2D features

Potential applications in face recognition and animation

Abstract

Multiview 3D face modeling has attracted increasing attention recently and has become one of the potential avenues in future video systems. We aim to make more reliable and robust automatic feature extraction and natural 3D feature construction from 2D features detected on a pair of frontal and profile view face images. We propose several heuristic algorithms to minimize possible errors introduced by prevalent nonperfect orthogonal condition and noncoherent luminance. In our approach, we first extract the 2D features that are visible to both cameras in both views. Then, we estimate the coordinates of the features in the hidden profile view based on the visible features extracted in the two orthogonal views. Finally, based on the coordinates of the extracted features, we deform a 3D generic model to perform the desired 3D clone modeling. Present study proves the scope of resulted facial models for practical applications like face recognition and facial animation.