Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Gait

Age Classification Based on Gait Using HMM

We have proposed a new framework for age classification based on human gait using the Hidden Markov Model (HMM). To extract appropriate gait features, we consider a contour related method in terms of shape variations during human walking. Then the image feature is transformed to a lower-dimensional space by using the Frame to Exemplar (FED) distance. A HMM is trained on the FED vector sequences. Thus, the framework provides flexibility in the selection of gait feature representation. In addition, the framework is robust for classification due to the statistical nature of HMM. The experimental results show that video-based automatic age classification from human gait is feasible and reliable.

Ethnicity Classification Based on Gait Using Multi-view Fusion

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. Gait Energy Image (GEI) is used in this research to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x; y; view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs the best.

Human recognition in a video network

Video networks is an emerging interdisciplinary field with significant and exciting scientific and technological challenges. It has great promise in solving many real-world problems and enabling a broad range of applications, including smart homes, video surveillance, environment and traffic monitoring, elderly care, intelligent environments, and entertainment in public and private spaces. This paper provides an overview of the design of a wireless video network as an experimental environment, camera selection, hand-off and control, anomaly detection. It addresses challenging questions for individual identification using gait and face at a distance and present new techniques and their comparison for robust identification.

Recognition of Walking Humans in 3D: Initial Result

It has been challenging to recognize walking humans at arbitrary poses from a single or small number of video cameras. Attempts have been made mostly using a 2D image/silhouette-based representation and a limited use of 3D kinematic model-based approaches. Unlike all the previous work in computer vision and pattern recognition, the models of walking humans are built using the sensed 3D range data at selected poses without any markers. An instance of a walking individual at a different pose is recognized using the 3D range data of that pose. Both modeling and recognition of an individual are done using the dense 3D range data. The proposed approach first measures 3D human body data that consists of the representative poses during a gait cycle. Next, a 3D human body model is fitted to the body data using an approach that overcomes the inherent gaps in the data and estimates the body pose with high accuracy. A gait sequence is synthesized by interpolation of joint positions and their movements from the fitted body models. Both dynamic and static gait features are obtained which are used to define a similarity measure for an individual recognition in the database. The experimental results show high recognition rates using our range based 3D gait database.

Human Recognition at a Distance

This paper consider face, side face, gait and ear and their possible fusion for human recognition. It presents an overview of some of the techniques that we have developed for (a) super-resoulution-based face recognition in video, (b) gait-based recognition in video, (c) fusion of super-resolved side face and gait in video, (d) ear recognition in color/range images, and (e) fusion performance prediction and validation. It presents various real-world examples to illustrate the ideas and points out the relative merits of the approaches that are discussed.

Feature fusion of side face and gait for video-based human identification

Video-based human recognition at a distance remains a challenging problem for the fusion of multimodal biometrics. We present a new approach that utilizes and integrates information from side face and gait at the feature level. The features of face and gait are obtained separately using principal component analysis (PCA) from enhanced side face image (ESFI) and gait energy image (GEI), respectively. Multiple discriminant analysis (MDA) is employed on the concatenated features of face and gait to obtain discriminating synthetic features. The experimental results demonstrate that the synthetic features, encoding both side face and gait information, carry more discriminating power than the individual biometrics features, and the proposed feature level fusion scheme outperforms the match score level and another feature level fusion scheme.

Integrating Face and Gait for Human Recognition at a Distance in Video

We have introduced a new video-based recognition method to recognize noncooperating individuals at a distance in video who expose side views to the camera. Information from two biometrics sources, side face and gait, is utilized and integrated for recognition. For side face, an enhanced side-face image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, is constructed. For gait, the gait energy image (GEI), a spatiotemporal compact representation of gait in video, is used to characterize human-walking properties. The experimental results show that the idea of constructing ESFI from multiple frames is promising for human recognition in video, and better face features are extracted from ESFI compared to those from the original side-face images (OSFIs).

Individual Recognition Using Gait Energy Image

We propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we generate a series of new GEI templates by analyzing the human silhouette distortion under various conditions. Principal component analysis followed by multiple discriminant analysis are used for learning features from the expanded GEI training templates. Recognition is carried out based on the learned features. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to current gait recognition approaches.

Fusion of Color and Infrared Video for Moving Human Detection

Image registration between color and thermal images is a challenging problem due to the difficulties associated with finding correspondence. However, the moving people in a static scene provide cues to address this problem. We propose a hierarchical scheme to automatically find the correspondence between the preliminary human silhouettes extracted from synchronous color and thermal image sequences for image registration. It is shown that the proposed approach achieves good results for image registration and human silhouette extraction. Experimental results also show a comparison of various sensor fusion strategies and demonstrate the improvement in performance over non-fused cases for human silhouette extraction.

Feature Fusion of Face and Gait for Human Recognition at a Distance in Video

A new video based recognition method is presented to recognize non-cooperating individuals at a distance in video, who expose side views to the camera. Information from two biometric sources, side face and gait, is utilized and integrated at feature level. For face, a high-resolution side face image is constructed from multiple video frames. For gait, Gait Energy Image (GEI), a spatio-temporal compact representation of gait in video, is used to characterize human walking properties. Face features and gait features are obtained separately using Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) combined method from the high-resolution side face image and Gait Energy Image (GEI), respectively. The system is tested on a database of video sequences corresponding to 46 people. The results showed that the integrated face and gait features carry the most discriminating power compared to any individual biometric.

Human Activity Classification Based on Gait Energy Image and Coevolutionary Genetic Programming

We present a novel approach based on gait energy image (GEI) and co-evolutionary genetic programming (CGP) for human activity classification. Specifically, Hu’s moment and normalized histogram bins are extracted from the original GEIs as input features. CGP is employed to reduce the feature dimensionality and learn the classifiers. The strategy of majority voting is applied to the CGP to improve the overall performance in consideration of the diversification of genetic programming. This learning based approach improves the classification accuracy by approximately 7 percent in comparison to the traditional classifiers.

Integrating Face and Gait for Human Recognition

We introduce a new video based recognition method to recognize non-cooperating individuals at a distance in video, who expose side views to the camera. Information from two biometric sources, side face and gait, is utilized and integrated for recognition. For side face, we construct Enhanced Side Face Image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, to fuse information of face from multiple video frames. For gait, we use Gait Energy Image (GEI), a spatio-temporal compact representation of gait in video, to characterize human walking properties. The features of face and the features of gait are obtained separately using Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) combined method from ESFI and GEI, respectively. They are then integrated at match score level. Our approach is tested on a database of video sequences corresponding to 46 people. The different fusion methods are compared and analyzed. The experimental results show that (a) Integrated information from side face and gait is effective for human recognition in video; (b) The idea of constructing ESFI from multiple frames is promising for human recognition in video and better face features are extracted from ESFI compared to those from original face images.

A Study on View-insensitive Gait Recognition

Most gait recognition approaches only study human walking fronto parallel to the image plane which is not realistic in video surveillance applications. Human gait appearance depends on various factors including locations of the camera and the person, the camera axis and the walking direction. By analyzing these factors, we propose a statistical approach for view-insensitive gait recognition. The proposed approach recognizes human using a single camera, and avoids the difficulties of recovering the human body structure and camera calibration. Experimental results show that the proposed approach achieves good performance in recognizing individuals walking along different directions.

Human recognition at a distance in video by integrating face profile and gait

Human recognition from arbitrary views is an important task for many applications, such as visual surveillance, covert security and access control. It has been found to be very difficult in reality, especially when a person is walking at a distance in read-world outdoor conditions. For optimal performance, the system should use as much information as possible from the observations. In this paper, we propose an innovative system, which combines cues of face profile and gait silhouette from the single camera video sequences. For optimal face profile recognition, we first reconstruct a high-resolution face profile image from several adjacent low-resolution video frames. Then we use a curvature-based matching method for recognition. For gait, we use Gait Energy Image (GEI) to characterize human walking properties. Recognition is carried out based on the direct GEI matching. Several schemes are considered for fusion of face profile and gait. A number of dynamic video sequences are tested to evaluate the performance of our system. Experiment results are compared and discussed.

Gait recognition by combining classifiers based on environmental contexts

Human gait properties can be affected by various environmental contexts such as walking surface and carrying objects. In this paper, we propose a novel approach for individual recognition by combining different gait classifiers with the knowledge of environmental contexts to improve the recognition performance. Different classifiers are designed to handle different environmental contexts, and context specific features are explored for context characterization. In the recognition procedure, we can determine the probability of environmental contexts in any probe sequence according to its context features, and apply the probabilistic classifier combination strategies for the recognition. Experimental results demonstrate the effectiveness of the proposed approach.

Human Activity Recognition in Thermal Infrared Imagery

Here we investigate human repetitive activity properties from thermal infrared imagery, where human motion can be easily detected from the background regardless of lighting conditions and colors of the human surfaces and backgrounds. We employ an efficient spatio-temporal representation for human repetitive activity recognition, which represents human motion sequence in a single image while preserving some temporal information. A statistical approach is used to extract features for activity recognition. Experimental results show that the proposed approach achieves good performance for human repetitive activity recognition.

Performance Prediction for Individual Recognition by Gait

Existing gait recognition approaches do not give their theoretical or experimental performance predictions. Therefore, the discriminating power of gait as a feature for human recognition cannot be evaluated. We propose a kinematic-based approach to recognize human by gait. The proposed approach estimates 3D human walking parameters by performing a least squares fit of the 3D kinematic model to the 2D silhouette extracted from a monocular image sequence.

Individual Recognition Using Gait Energy Image

In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we generate a series of new GEI templates by analyzing the human silhouette distortion under various conditions. Principal component analysis followed by multiple discriminant analysis are used for learning features from the expanded GEI training templates. Recognition is carried out based on the learned features. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to current gait recognition approaches.

Statistical Feature Fusion for Gait-based Human Recognition

This paper presents a novel approach for human recognition by combining statistical gait features from real and synthetic templates. Real templates are directly computed from training silhouette sequences, while synthetic templates are generated from training sequences by simulating silhouette distortion. A statistical feature extraction approach is used for learning effective features from real and synthetic templates. Features learned from real templates characterize human walking properties provided in training sequences, and features learned from synthetic templates predict gait properties under other conditions. A feature fusion strategy is therefore applied at the decision level to improve recognition performance. We apply the proposed approach to USF HumanID Database. Experimental results demonstrate that the proposed fusion approach not only achieves better performance than individual approaches, but also provides large improvement in performance with respect to the baseline algorithm.

Gait Energy Image Representation: Comparative Performance Evaluation on USF HumanID Database

A new spatio-temporal gait representation, called Gait Energy Image (GEI), is proposed to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we expand the training templates by analyzing the human silhouette distortion under various conditions. Principal component analysis and multiple discriminant analysis are used for learning features from the expanded GEI based gait recognition approaches with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to current gait recognition approaches.

Human Recognition on Combining Kinematic and Stationary Features

Both the human motion characteristics and body part measurement are important cues for human recognition at a distance. The former can be viewed as kinematic measurement while the latter is stationary measurement. We propose a kinematic based approach to extract both kinematic and stationary features for human recognition. The proposed approach first estimates 3D human walking parameters by fitting the 3D kinematic model to the 2D silhouette extracted from a monocular image sequence. Kinematic and stationary features are then extracted from the kinematic and stationary parameters, respectively, and used for human recognition separately. Next, we discuss different strategies for combining kinematic and stationary features to make a decision. Experimental results show a comparison of these combination strategies and demonstrate the improvement in performance for human recognition.

Bayesian-based performance prediction for gait recognition

Existing gait recognition approaches do not give their theoretical or experiential performance predictions. Therefore, the discriminating power of gait as a feature for human recognition cannot be evaluated. We first propose a kinematic-based approach to recognize humans by gait. The proposed. approach estimates 3D human walking parameters by performing a least squares fit of the 3D kinematic model to the 2D silhouette extracted from a monocular image sequence. Next, a Bayesian based statistical analysis is performed to evaluate the discriminating power of the extracted features. Through probabilistic simulation, we not only predict the probability of correct recognition (PCR) with regard to different within-class feature variance, but also obtain the upper bound on PCR with regard to different human silhouette resolutions. In addition, the maximum number of people in a database is obtained given the allowable error rate. This is extremely important for gait recognition in large databases.

Kinematic-based human motion analysis in infrared sequences

In an infrared (IR) image sequence of human walking, the human silhouette can be reliably extracted from the background regardless of lighting conditions and colors of the human surfaces and backgrounds in most cases. Moreover, some important regions containing skin, such as face and hands, can be accurately detected in IR image sequences. In this paper, we propose a kinematic-based approach for automatic human motion analysis from IR image sequences. The proposed approach estimates 3D human walking parameters by performing a modified least squares fit of the 3D kinematic model to the 2D silhouette extracted from a monocular IR image sequence, where continuity and symmetry of human walking and detected hand regions are also considered in the optimization function. Experimental results show that the proposed approach achieves good performance in gait analysis with different view angles With respect to the walking direction, and is promising for further gait recognition.

Individual recognition by kinematic-based gait analysis

Current gait recognition approaches only consider individuals walking frontopamllel to the image plane. This makes them inapplicoble for recognizing individuals walking from different angles with respect to the image plane. In this paper, we propose a kinematic-based approach to recognize individuals by gait. The proposed approach estimates 30 human walking parameters by performing a least squares fit of the 30 kinematic model to the 20 silhouette eztmctedfmm a monocular image sequence. A genetic algorithm is used for feature selection from the estimated parameters, and the individuals are then recognized from the feature vectors using a nearest neighbor method. Ezperimental results show that the proposed approach achieves good performance in recognizing individuals walking fmm different angles with respect to the image plane.