CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
A Multi-view Method for Gait Recognition Using Static Body Parameters
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
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Designs, Codes and Cryptography
Improved Gait Recognition by Gait Dynamics Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait recognition using linear time normalization
Pattern Recognition
Distributed Grayscale Stereo Image Coding with Unsupervised Learning of Disparity
DCC '07 Proceedings of the 2007 Data Compression Conference
A full-body layered deformable model for automatic model-based gait recognition
EURASIP Journal on Advances in Signal Processing
Unobtrusive multimodal biometric authentication: the HUMABIO project concept
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing
Computer Security: Principles and Practice
Computer Security: Principles and Practice
Zernike velocity moments for sequence-based description of moving features
Image and Vision Computing
Gait analysis for human identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Advances in automatic gait recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Gait Recognition Using Compact Feature Extraction Transforms and Depth Information
IEEE Transactions on Information Forensics and Security - Part 2
Protecting Biometric Templates With Sketch: Theory and Practice
IEEE Transactions on Information Forensics and Security - Part 2
Distributed source coding using syndromes (DISCUS): design and construction
IEEE Transactions on Information Theory
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
Identification of humans using gait
IEEE Transactions on Image Processing
Gait Recognition Using Radon Transform and Linear Discriminant Analysis
IEEE Transactions on Image Processing
Robust image watermarking based on generalized Radon transformations
IEEE Transactions on Circuits and Systems for Video Technology
Gait-based human age estimation
IEEE Transactions on Information Forensics and Security
Multi-biometrics based crypto-biometric session key generation and sharing protocol
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
| Hi-index | 0.00 |
Human authentication using biometric traits has become an increasingly important issue in a large range of applications. In this paper, a novel channel coding approach for biometric authentication based on distributed source coding principles is proposed. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder and error correcting codes are employed for user verification. It is shown that the effective exploitation of the noise channel distribution in the decoding process improves performance. Moreover, the proposed method increases the security of the stored biometric templates. As a case study, the proposed framework is employed for the development of a novel gait recognition system based on the extraction of depth data from human silhouettes and a set of discriminative features. Specifically, gait sequences are represented using the radial and the circular integration transforms and features based on weighted Krawtchouk moments. Analytical models are derived for the effective modeling of the correlation channel statistics based on these features and integrated in the soft decoding process of the channel decoder. The experimental results demonstrate the validity of the proposed method over state-of-the-art techniques for gait recognition.