Universal approximation using radial-basis-function networks
Neural Computation
Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
The handbook of brain theory and neural networks
EigenGait: Motion-Based Recognition of People Using Image Self-Similarity
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Automatic gait recognition by symmetry analysis
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Silhouette-Based Human Identification from Body Shape and Gait
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Baseline Results for the Challenge Problem of Human ID Using Gait Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Extracting Human Gait Signatures by Body Segment Properties
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human gait recognition at sagittal plane
Image and Vision Computing
Gait recognition using fractal scale
Pattern Analysis & Applications
Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion
Pattern Recognition Letters
Extracting a diagnostic gait signature
Pattern Recognition
Model-based human gait recognition using fusion of features
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Deterministic Learning Theory for Identification, Recognition, and Control
Deterministic Learning Theory for Identification, Recognition, and Control
Model-based human gait recognition using leg and arm movements
Engineering Applications of Artificial Intelligence
Gait flow image: A silhouette-based gait representation for human identification
Pattern Recognition
On automated model-based extraction and analysis of gait
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A novel gait recognition method via fusing shape and kinematics features
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
Stability and approximator convergence in nonparametric nonlinear adaptive control
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Deterministic Learning and Rapid Dynamical Pattern Recognition
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Rapid Detection of Small Oscillation Faults via Deterministic Learning
IEEE Transactions on Neural Networks
Silhouette-Based gait recognition via deterministic learning
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
A speed invariant human identification system using gait biometrics
International Journal of Computational Vision and Robotics
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Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. Human gait recognition is a typical difficulty in the area of dynamical pattern recognition. It classifies and identifies individuals by their time-varying gait signature data. Recently, a new dynamical pattern recognition method based on deterministic learning theory was presented, in which a time-varying dynamical pattern can be effectively represented in a time-invariant manner and can be rapidly recognized. In this paper, we present a new model-based approach for human gait recognition via the aforementioned method, specifically for recognizing people by gait. The approach consists of two phases: a training (learning) phase and a test (recognition) phase. In the training phase, side silhouette lower limb joint angles and angular velocities are selected as gait features. A five-link biped model for human gait locomotion is employed to demonstrate that functions containing joint angle and angular velocity state vectors characterize the gait system dynamics. Due to the quasi-periodic and symmetrical characteristics of human gait, the gait system dynamics can be simplified to be described by functions of joint angles and angular velocities of one side of the human body, thus the feature dimension is effectively reduced. Locally-accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks (NNs) through deterministic learning. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles versus angular velocities. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. In the test phase, by comparing the set of estimators with the test gait pattern, a set of recognition errors are generated, and the average L"1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern similar to one of the training gait patterns can be rapidly recognized according to the smallest error principle. Finally, experiments are carried out on the NLPR and UCSD gait databases to demonstrate the effectiveness of the proposed approach.