Gait recognition based on gait pal and pal entropy image

Medikonda, Jeevan; Jain, Neha; Hanmandlu, M.; Chetty, Girija

doi:10.1109/icip.2013.6738864

Cited by 53 publications

(25 citation statements)

References 19 publications

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“…Thus, the proposed representation technique is capable of capturing variations without increasing the size of the knowledge-base. Also observe that the ACCR (88.99% for different covariates in training set) and ACCR (79.01% for similar covariates in training set) obtained from the proposed system for CASIA B data set is significantly high when compared to the ACCR (78.80%) reported by Dupuis [6] and ACCR (70.24%) reported by Jeevan [3] for similar covariates in training set for the same data set.…”

Section: Discussionmentioning

confidence: 72%

“…4 shows that the performance at rank 1 is the correct classification rate (CCR) and we have achieved average CCR of 79.01%. Table 6 shows the recognition performance (% of average correct classification rate at rank 1) of experiment I and experiment II as discussed in sub section 4.2.1 and 4.2.2 respectively of the proposed method and other methods reported in [6] and [3]. Table 6.…”

Section: Experiments IImentioning

confidence: 99%

“…Experiment is conducted on 30 subjects and author claimed acceptable results. Jeevan et al [3] proposed gait recognition using Pal and Pal Entropy. PCA is applied on the feature matrix and Support Vector Machine is used for classification.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Kumar¹,

Nagendraswamy²

2014

SIPIJ

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Gait is one of the biometric techniques used to identify an individual from a distance by his/her walking style. Gait can be recognized by studying the static and dynamic part variations of individual body contour during walk. In this paper, an interval value based representation and recognition of gait using local binary pattern (LBP) of split gait energy images is proposed. The gait energy image (GEI) of a subject is split into four equal regions. LBP technique is applied to each region to extract features and the extracted features are well organized. The proposed representation technique is capable of capturing variations in gait due to change in cloth, carrying a bag and different instances of normal walking conditions more effectively. Experiments are conducted on the standard and considerably large database (CASIA database B) and newly created University of Mysore (UOM) gait dataset to study the efficacy of the proposed gait recognition system. The proposed system being robust to handle variations has shown significant improvement in recognition rate.

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Section: Discussionmentioning

confidence: 72%

Section: Experiments IImentioning

confidence: 99%

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Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Kumar¹,

Nagendraswamy²

2014

SIPIJ

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“…Over past 30 years, many researchers have proposed different gait recognition techniques motivated by the real world applications requiring the recognition of the human gait. The approach of gait recognition aims to detect the gait of a person in a still image or a video having many methods such as hybrid, global or local approach [2]. Many analyses have been observed in the past but the recognition of human gait is not accurately and appropriately conducted.…”

Section: Introductionmentioning

confidence: 99%

Hybrid method for Gait recognition using SVM and Baysian Network

Gupta

Prasad

Alsadoon

et al. 2015

2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)

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The Gait recognition is the 2nd generation of biometric identification technology which aims to identify people at a distance by the way they walk. Due to the fact that there has been increasing research interest in the identification of an individual in access controlled environments such as the airports, banks and car parks, it has been observed that the effective human gait recognition plays a very important role in such video surveillance based applications .This paper proposes an effective Gait recognition method for automatic person recognition using SVM and Bayesian Network. In this method frames of videos are used as an input, these videos are live and are from the CASIA dataset. The background subtraction is done using Gait Pal and Pal Entropy and a Median Filter is also used to remove noise from the background. Feature selection is done using the Hanavan's model to reduce the computational cost during training and recognition. Support Vector Machine (SVM) and Bayesian Network are used for training and testing purpose. The experimental results show that the proposed approach has a very effective Correct Classification rate (CCR).

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“…[32], Gait Pal and Pal Entropy [33] and Gait Entropy Image. The better performance of the Gait Recognition method on Spatio -Temporal Feature Domain is given below in Table 11.…”

mentioning

confidence: 99%

Survey of Current Trends in Human Gait Recognition Approaches.

Ijar¹,

Raj²,

Balamurugan³

2017

IJAR

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Introduction:-Gait is one of the few biometric features that can be measured remotely without physical contact and proximal sensing, which makes it useful in surveillance applications. Such applications play a decisive role in monitoring high security areas including banks, airports, military bases and railway stations. In the real world, there are various factors, significantly affecting human gait including clothes, shoes, carrying objects, walking surfaces, walking speeds and observed views. A large number of gait recognition methods have been published recently, which can be roughly divided into two categories, model-based methods include "A new view-invariant feature for cross-view gait recognition" and appearance-based method include "Recognizing gaits across views through correlated motion co-clustering". These methods require a preprocessing of foreground/background segmentation (FG/BG) on a gait video, in order to extract shape contours, silhouettes, skeletons, or body joints for further gait analysis. The modelbased methods generally aim to model kinematics of human joints in order to measure physical gait parameters such as trajectories, limb lengths and angular speeds. The appearance-based methods typically analyze gait sequences without explicit modeling of human body structure. These methods have shown their effectiveness on human gait recognition under fixed view. However, they lack a proper methodology to address the problem of view change.

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Gait recognition based on gait pal and pal entropy image

Cited by 53 publications

References 19 publications

Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Symbolic Representation and Recognition of GAIT : An Approach Based on LBP of Split GAIT Energy Images

Hybrid method for Gait recognition using SVM and Baysian Network

Survey of Current Trends in Human Gait Recognition Approaches.

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