Shihong Lao

Face alignment is a critical problem in many face related applications such as facial expression analysis,face recognition,etc.In this paper,we use local textures of each label point to predict the displacement of each label point by applying nonlinear boosting regression based on Haar rectangle feature,and develop a novel real-time multi-view face alignment system based on the active shape model.Experiments on two independent datasets show that our algorithm is much faster,more accurate and robust than the classic active shape model and outperforms recently improved algorithm,too.It has significant practical value in real applications.

Tracking object in low frame rate video or with abrupt motion poses two main difficulties which conventional tracking methods can barely handle: 1) poor motion continuity and increased search space; 2) fast appearance variation of target and more background clutter due to increased search space. In this paper, we address the problem from a view which integrates conventional tracking and detection, and present a temporal probabilistic combination of discriminative observers of different lifespans. Each observer is learned from different ranges of samples, with different subsets of features, to achieve varying level of discriminative power at varying cost. An efficient fusion and temporal inference is then done by a cascade particle filter which consists of multiple stages of importance sampling. Experiments show significantly improved accuracy of the proposed approach in comparison with existing tracking methods, under the condition of low frame rate data and abrupt motion of both target and camera.

Scale invariant feature transform (SIFT) proposed by Lowe has been widely and successfully applied to object detection and recognition. However, the representation ability of SIFT features in face recognition has rarely been investigated systematically. In this paper, we proposed to use the person-specific SIFT features and a simple non-statistical matching strategy combined with local and global similarity on key-points clusters to solve face recognition problems. Large scale experiments on FERET and CAS-PEAL face databases using only one training sample per person have been carried out to compare it with other non person-specific features such as Gabor wavelet feature and local binary pattern feature. The experimental results demonstrate the robustness of SIFT features to expression, accessory and pose variations.

We present a real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner. The approach requires no extra hardware except for a generic webcamera. Eyeblink sequences often have a complex underlying structure. We formulate blink detection as inference in an undirected conditional graphical framework, and are able to learn a compact and efficient observation and transition potentials from data. For purpose of quick and accurate recognition of the blink behavior, eye closity, an easily-computed discriminative measure derived from the adaptive boosting algorithm, is developed, and then smoothly embedded into the conditional model. An extensive set of experiments are presented to show effectiveness of our approach and how it outperforms the cascaded Adaboost and HMM in task of eyeblink detection.

On-line discovery of some auxiliary objects to verify the tracking results is a novel approach to achieving robust tracking by balancing the need for strong verification and computational efficiency. However, the applicability and effectiveness of this approach highly depend on how to reliably validate the motion correlation between the target and the auxiliary objects so as to estimate the motion model. In this paper, we extend the algorithm of mining auxiliary objects for tracking by incorporating multibody grouping to detect the motion correlation and estimate the motion model, which imposes more general motion correlation constraints. The proposed method discovers the auxiliary objects that exhibit strong affine motion correlation and estimates the closed-form affine models. The proposed tracking algorithm shows good performance in real-world test sequences.