Biometric Authorization System Using Gait Biometry

Sudha,; Bhavani,

doi:10.5121/ijcsea.2011.1401

Cited by 13 publications

(7 citation statements)

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“…L.R Sudha, R. Bhavani. [5] Proposed multiple gait components, i.e., spatial, temporal and wavelet are extracted from the silhouette gait sequences and fused for the development of a high performance classification system. Temporal features gives poor performance while using separately, improves performance rate when fused with other features with 97.91% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition Based on Silhouettes Sequences and Neural Networks for Human Identification

Mohualdeen

Baker

2018

IJEEI

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Human gait recognition is moving ahead by the need for automated person identification and verification at a distance in many applications. In this paper our system presented silhouette videos and neural networks based system for human identification. For each sequence of silhouette images, an automated Region of Interest (ROI) algorithm applied to reduce dimensionality, extract gait features has been attempted with 3level 2Dimension Discrete Wavelet Transform (3L-2D-DWT), edges detection and gait cycle were used to extract relevant feature. Back propagations neural networks used as pattern recognition. The M ATLAB software graphics user interface is designed to display result and simplify the use of the system. The developed system has been evaluated using (TUM-IITKGP) which contains three different type of walking categories and the results demonstrate that the proposed system achieves 98.8%, 95.87% and 88% for normal walk, walking with backpack and walking with hand in pocket category respectively of correct recognition. We concluded that the different side of view movement increase reliability of the key extracted feature and improve the neural network performance which opens a scope for further development.

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Section: Introductionmentioning

confidence: 99%

Gait Recognition Based on Silhouettes Sequences and Neural Networks for Human Identification

Mohualdeen

Baker

2018

IJEEI

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“…The method is tested on outdoor sequences of 44 people with 4 sequences of each taken on two different days, and achieves a classification rate of 77%. Paper [7] proposed a system which is evaluated using side view videos of NLPR Database and experimental results indicate that classification ability of SVM with Radial Basis Function (RBF) is better than other Kernel function.…”

Section: Introductionmentioning

confidence: 99%

An Approach of Human Identification based on Gait

Gangotia¹,

Shrivastava²,

Singh³

2014

IJCA

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Gait is a behavioral biometrics which has gains more attention for research. Advantages of human gait are that it can be captured from a distance without knowledge of the subject. Gait is defined as manner of person's walking. A wide range of research have been done in this filed so far. In our research work, an approach has been proposed for identification of human being based on their gait. We have evaluated result analysis on CASIA Dataset. We have considered right side view of 17 subjects and three features for result evaluation. In this paper two type of analysis have been done and find out which analysis have given better recognition rate.

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“…The state-space methods consider gait motion to be composed of a sequence of static body poses and recognize it by considering temporal variation observations. The spatiotemporal method characterizes the spatiotemporal distribution by collapsing the entire 3D spatiotemporal data over an entire sequence into 1D or 2D signals [13].…”

Section: Figure 1 Classification Of Biometrics Technologiesmentioning

confidence: 99%

“…In model based approach , the human body structure or motion is modeled first and then the image features are extracted by measuring the structural components of models or by measuring the motion trajectories [6] [11]. Motion based approach is further classified into statespace methods and spatiotemporal methods [12] [13]. The state-space methods consider gait motion to be composed of a sequence of static body poses and recognize it by considering temporal variation observations.…”

Section: Figure 1 Classification Of Biometrics Technologiesmentioning

confidence: 99%