Using Gait as a Biometric, via Phase-weighted Magnitude Spectra
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
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
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
On automated model-based extraction and analysis of gait
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Identification of humans using gait
IEEE Transactions on Image Processing
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This paper presents a gait recognition algorithm for human identification from a sequence of segmented noisy silhouettes in a low-resolution video. The main contribution of the proposed work is the use of the hierarchical recovery of a static body and stride parameters of model subjects to the walking pose. The proposed algorithm overcomes drawbacks of existing works by extracting a set of relative model parameters instead of directly analyzing the gait pattern. The feature extraction function in the proposed algorithm consists of motion detection, object region detection, and active shape model (ASM), which alleviate problem in the baseline algorithm such as; background generation, shadow removal, and higher recognition rate. Performance of the proposed algorithm has been evaluated by using the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition, and time.