Distinguishing between identical twins is the Holy Grail in face recognition because of the great similarity between the faces of a pair of twins. Most existing face recognition systems choose to simply ignore it. However, as the population of twins increases quickly, such an “ostrich strategy” is no longer acceptable. The biometric systems that overlook the twins problem are presenting a serious security hole. Inspired by recent advances in motion-based face recognition techniques, we propose to use facial motion to address the twins problem. We collect a twins facial expression database and conduct a series of experiments in two assumed scenarios: the Social Party Scenario and the Access Control Scenario. The experimental results show that facial motion ourperforms facial appearance in distinguishing between twins. Based on this finding, we propose a two-stage cascaded General Access Control System, which combines facial appearance with facial motion. The experimental results show that, compared with an appearance-based face recognition system, this cascaded system is much more secure against an “evil-twin” imposter attack, while performing as good for normal population.

Many existing techniques for analyzing face images assume that the faces are at nearly frontal. Generalizing to non-frontal faces is often difficult, due to a dearth of ground truth for non-frontal faces and also to the inherent challenges in handling pose variations. In this work, we investigate how to learn a universal multi-view age estimator by harnessing 1) unlabeled web videos, 2) a publicly available labeled frontal face corpus, and 3) zero or more non-frontal faces with age labels. First, a large diverse human-involved video corpus is collected from online video sharing website. Then, multi-view face detection and tracking are performed to build a large set of frontal-vs-profile face bundles, each of which is from the same tracking sequence, and thus exhibiting the same age. These unlabeled face bundles constitute the so-called video context, and the parametric multi-view age estimator is trained by 1) enforcing the face-to-age relation for the partially labeled faces, 2) imposing the consistency of the predicted ages for the non-frontal and frontal faces within each face bundle, and 3) mutually constraining the multi-view age models with the spatial correspondence priors derived from the face bundles. Our multi-view age estimator performs well on a realistic evaluation dataset that contains faces under varying poses, and whose ground truth age was manually annotated.

This paper introduces a method to correct over-exposure in an existing photograph by recovering the color and lightness separately. First, the dynamic range of well exposed region is slightly compressed to make room for the recovered lightness of the over-exposed region. Then the lightness is recovered based on an over-exposure likelihood. The color of each pixel is corrected via neighborhood propagation and also based on the confidence of the original color. Previous methods make use of ratios between different color channels to recover the over-exposed ones, and thus can not handle regions where all three channels are over-exposed. In contrast, our method does not have this limitation. Our method is fully automatic and requires only one single input photo. We also provide users with the flexibility to control the amount of over-exposure correction. Experiment results demonstrate the effectiveness of the proposed method in correcting over-exposure.

10.1109/CVPR.2010.5539941

This paper introduces an approach of creating face makeup upon a face image with another image as the style example. Our approach is analogous to physical makeup, as we modify the color and skin detail while preserving the face structure. More precisely, we first decompose the two images into three layers: face structure layer, skin detail layer, and color layer. Thereafter, we transfer information from each layer of one image to corresponding layer of the other image. One major advantage of the proposed method lies in that only one example image is required. This renders face makeup by example very convenient and practical. Equally, this enables some additional interesting applications, such as applying makeup by a portraiture. The experiment results demonstrate the effectiveness of the proposed approach in faithfully transferring makeup.