Total views : 838
Gait Based Human Identification in Bad Illumination
Vision-based human identification in bad illumination is one of the challenge. Gait based human identification is an important biometric feature. In order to study and analyze Gait recognition in bad illumination conditions a framework is proposed. Kinect sensor is used for signal extraction, which has the capability to track human in bad illumination. Database is created using 5 persons by considering good illumination conditions and bad illumination conditions. The features of each individual are extracted using skeleton information. Mean value of distance between centroid of left leg lower part and centroid of right leg lower part is proposed as a new feature for Gait recognition. Training and classification is performed using Levenberg-Marquardt back propagation algorithm and SVM algorithm. Mean value of distance between centroid of left leg lower part and centroid of right leg lower part provides good distinguishing values between different persons. Results obtained for both algorithms are tabulated. 82.66% recognition rate achieved with Levenberg-Marquardt back propagation algorithm where as 92% recognition rate achieved with SVM for 5 persons in bad illumination conditions, with fixed Kinect sensor set up. From results it is revealed that the proposed framework performs the gait based human identification in bad illumination.
Bad Illumination, Feature Extraction, Gait Recognition, Recognition Rate, Skeleton Information, SVM.
- Murray MP, Drought AB, Kory RC. Walking patterns of normal men. Journal of Bone and Joint Surgery. 1964; 46A(2):335–60.
- Murray MP. Gait as a total pattern of movement. American Journal of Physical Medicine. 1967; 46(1):290–332. PMid:5336886
- Collins R, Gross R, Shi J. Silhouette-based human Identification from body shape and gait. Proceedings of 5th IEEE International Conference Automatic Face and Gesture Recognition; USA. 2012. p. 366–71.
- Liang Wang, Huazhong Ning, Tieniu Tan, Weiming Hu. Fusion of Static and Dynamic Body Biometrics for Gait Recognition.IEEE Transactions onCircuits andSystems forVideo Technology. 2004, 14 (2), pp. 149-158.
- Prathap C, Sakkara S. Gait recognition using skeleton data. Proceedings of 4th ICACCI; India. 2015. p. 2302–6.
- Kinect SDK. Crossref
- Abdul Rahman Hafiz, Md Faijul Amin,Kazuyuki Murase. Using Complex-Valued Levenberg-Marquardt Algorithm for Learning and Recognizing Various Hand Gestures. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), Australia, 2012. p. 1-5.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.