Tracking multiple people with recovery from partial and total occlusion

Robust tracking of multiple people in video sequences is a challenging task. In this paper, we present an algorithm for tracking faces of multiple people even in cases of total occlusion. Faces are detected first; then a model for each person is built. The models are handed over to the tracking module which is based on the mean shift algorithm, where each face is represented by the non-parametric distribution of the colors in the face region. The mean shift tracking algorithm is robust to partial occlusion and rotation, and is computationally efficient, but it does not deal with the problem of total occlusion. Our algorithm overcomes this problem by detecting the occlusion using an occlusion grid, and uses a non-parametric distribution of the color of the occluded person's cloth to distinguish that person after the occlusion ends. Our algorithm uses the speed and the trajectory of each occluded person to predict the locations that should be searched after occlusion ends. It integrates multiple features to handle tracking multiple people in cases of partial and total occlusion. Experiments on a large set of video clips demonstrate the robustness of the algorithm, and its capability to correctly track multiple people even when faces are temporarily occluded by other faces or by other objects in the scene.     

Automatic facial feature extraction and 3D face modeling using two orthogonal views with application to 3D face recognition

We present a fully automated algorithm for facial feature extraction and 3D face modeling from a pair of orthogonal frontal and profile view images of a person's face taken by calibrated cameras. The algorithm starts by automatically extracting corresponding 2D landmark facial features from both view images, then compute their 3D coordinates. Further, we estimate the coordinates of the features that are hidden in the profile view based on the visible features extracted in the two orthogonal face images. The 3D coordinates of the selected feature points obtained from the images are used first to align, then to locally deform the corresponding facial vertices of the generic 3D model. Preliminary experiments to assess the applicability of the resulted models for face recognition show encouraging results.     

A multimodal approach for 3D face modeling and recognition using 3D deformable facial mask

We present a multimodal approach for face modeling and recognition. The algorithm uses three cameras to capture stereo images, two frontal and one profile, of the face. 2D facial features are extracted from one of the frontal images and a dense disparity map is computed from the two frontal images. Using the extracted 2D features and their corresponding disparities, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project its vertices onto the frontal 2D image and adjust its profile silhouette vertices using the profile view image. We increase the resolution of the resulting 2D model at its center region to obtain a facial mask model covering distinctive features of the face. The 2D coordinates of the vertices, along with their disparities, result in a deformed 3D mask model specific to a given subject’s face. Our method integrates information from the extracted facial features from the 2D image modality with information from the 3D modality obtained from the stereo images. Application of the models in 3D face recognition, for 112 subjects, validates the algorithm with a 95% identification rate and 92% verification rate at 0.1% false acceptance rate.