Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field.

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A Review of Vision-Based Gait Recognition Methods for Human Identification

Change detection and deformation monitoring is an active area of research within the field of engineering surveying and other overlapping areas such as structural and civil engineering. This paper reviews the application of terrestrial laser scanning in the monitoring of structures and discusses registration and georeferencing of scan data. Past terrestrial laser scanning research work has shown trends in addressing issues such as accurate registration and georeferencing of scans, error modelling, point cloud processing techniques for deformation analysis, scanner calibration and detection of millimetre deformations. However, several issues are still open to investigation such as robust methods of point cloud processing for detecting change and deformation, incorporation of measurement geometry in deformation measurements, design of data acquisition and quality assessment for precise measurements and modelling the environmental effects on the performance of laser scanning. A three-stage process model for ...

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A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

Recent developments and trends in point set registration methods

Cross-view gait recognition authenticates a person using a pair of gait image sequences with different observation views. View difference causes degradation of gait recognition accuracy, and so several solutions have been proposed to suppress this degradation. One useful solution is to apply a view transformation model (VTM) that encodes a joint subspace of multiview gait features trained with auxiliary data from multiple training subjects, who are different from test subjects (recognition targets). In the VTM framework, a gait feature with a destination view is generated from that with a source view by estimating a vector on the trained joint subspace, and gait features with the same destination view are compared for recognition. Although this framework improves recognition accuracy as a whole, the fit of the VTM depends on a given gait feature pair, and causes an inhomogeneously biased dissimilarity score. Because it is well known that normalization of such inhomogeneously biased scores improves recognition accuracy in general, we therefore propose a VTM incorporating a score normalization framework with quality measures that encode the degree of the bias. From a pair of gait features, we calculate two quality measures, and use them to calculate the posterior probability that both gait features originate from the same subjects together with the biased dissimilarity score. The proposed method was evaluated against two gait datasets, a large population gait dataset of over-ground walking (course dataset) and a treadmill gait dataset. The experimental results show that incorporating the quality measures contributes to accuracy improvement in many cross-view settings.

View Transformation Model Incorporating Quality Measures for Cross-View Gait Recognition

This paper describes real-time daylight based eye gaze tracking system. Proposed solution consists of two cameras, infrared markers and few electronic components. Usage of the popular Web cams makes the system inexpensive. In dual camera system one camera is used for pupil tracking while second one controls position of the head relative to the screen. Two detection algorithms have been developed and implemented - pupil detection an the screen to head position detection. Proposed solution is an attempt of creating an interface for handicapped users which they could use instead of mouse or keyboard.

Eye mouse for disabled

Scaling iterative closest point algorithm for registration of m-D point sets

Silhouette quality quantification for gait sequence analysis and recognition

Shape and texture are two aspects in facial expression synthesis. For the requirements of synthesized shape and texture are different, it is unsuitable to use same method to synthesize shape and texture. In this paper, we propose a Statistical Shape and Radio texture fusion model for facial expression sequence synthesis. In this framework, facial shape and texture had been processed separately, then they had been fused together to synthesize final result. The main contributes of this paper are: First propose statistical shape and radio texture fusion model, process shape and texture separately. Second introduce ASM model into facial shape synthesis, and construct expression model of ASM parameters. Third put forward a shape based sample select mechanism, fusion shape and texture process together. Experiment results demonstrate that the proposed model is more expressive and realistic than traditional methods, and can process batch input well.

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Facial Expression Sequence Synthesis Based on Shape and Texture Fusion Model

Varying illumination condition is a challenging problem for face recognition and synthesis. The illumination re-rendering technique allows aligning the illumination effects of facial images or relighting them as expected. In this paper, we propose an improved illumination re-rendering method based on more accurate mapping of facial images in the parametric illumination space. This will make the parameter-based illumination alignment more reliable. A clustering-based criterion is designed to evaluate its parameter estimation precision. To guide the image re-rendering between any a parameter pair, an intermediate image called the illumination transition image (ITI) is defined to represent both the illumination variation information and the person-specific facial shape features. The extensive experimental results verify the proposed method outperforms the quotient image approach on both parameter precision and rendering quality.

Illumination transition image: Parameter-based illumination estimation and re-rendering

Statistical model based facial expression synthesis methods are robust and easier to be used in real environment. But facial expressions of human are very various. How to represent and synthesize expressions which is not included in training set is an unresolved problem in statistical model based researches. In this paper, we propose a two step method. At first, we propose a statistical appearance model, the facial component model, to represent faces. The model divides the face into 7 components, and constructs one global shape model and 7 local texture models separately. The motivation to use global shape + local texture strategy is the combination of different components can generate much more kinds of expression than training set have and global shape guarantees to generate ‘legal’ result. Then a neighbor reconstruction framework was proposed to synthesize expressions. The framework estimates the target expression vector by linear combine of neighbor subject's expression vectors. This paper primarily contributes three things: First, the proposed method can synthesize a wider range of expressions than the training set have. Second, experimental demonstrate that FCM is better than standard AAM in face representation. Third, neighbor reconstruction framework is very flexible. It can be used in multi-samples with multi-targets and single-sample with single -target applications.

Lei Xiong

Papers

Scaling iterative closest point algorithm for registration of m-D point sets

Silhouette quality quantification for gait sequence analysis and recognition

Facial Expression Sequence Synthesis Based on Shape and Texture Fusion Model

Illumination transition image: Parameter-based illumination estimation and re-rendering

Extended facial expression synthesis using statistical appearance model