Multi-View Face Recognition From Single RGBD Models of the Faces

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks

On Channel Reliability Measure Training for Multi-Camera Face Recognition

Single-camera face recognition has severe limitations when the subject is not cooperative, or there are pose changes and different illumination conditions. Face recognition using multiple synchronized cameras is proposed to overcome the limitations. We introduce a reliability measure trained from examples to evaluate the inherent quality of channel recognition. The recognition from the channel predicted to be the most reliable is selected as the final recognition results. In this paper, we enhance Adaboost to improve the component based face detector running in each channel as well as the channel reliability measure training. Effective features are designed to train the channel reliability measure using data from both face detection and recognition. The recognition rate is far better than that of either single channel, and consistently better than common classifier fusion rules. 1