The analysis for Gait Energy Image based on statistical methods

Rahman, Siti Zaharah Abd.; Abdullah, Siti Norul Huda Sheikh; Nazri, Mohd Zakree Bin Ahmad

doi:10.1109/icaees.2016.7888022

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Dalal and Triggs introduced the HOG method in 2005, and it is useful in the early phase of object detection. This method is also used in the study of model-based model deformation which is an effective object tracking method for several years around 2008 to 2016 [10] and [23]- [25]. This increasingly recognizable method looks at features from all locations in the image and calculates based on the gradient information.…”

Section: Methodsmentioning

confidence: 99%

Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient

Rahman

Abdullah

Ariffin

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Gait recognition using the energy image representation of the average silhouette image in one complete cycle becomes a baseline in model-free approaches research. Nevertheless, gait is sensitive to any changes. Up to date in the area of feature extraction, image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat. Although the use of Histogram of orientation Gradient (HOG) in pedestrian detection is the most effective method, its accuracy is still considered low after testing on covariate dataset. Thus, this research proposed a combination of frequency and spatial features based on Inverse Fast Fourier Transform and Histogram of Oriented Gradient (IFFTG-HoG) for gait recognition. It consists of three phases, namely image processing phase, feature extraction phase in the production of new image representation and the classification. The first phase comprises the image binarization process and energy image generation using average gait image in one cycle. In the second phase, the IFFTG-HoG method is used as a features gait extraction after generating energy image. Here, the IFFTG-HoG method has also been improved by using Chebyshev distance to calculate the magnitude of the gradient to increase the rate of recognition accuracy. Lastly, K-Nearest Neighbour (k=NN) classifier with K=1 is employed for individual classification in the third phase. A total of 124 people from CASIA B dataset were tested using the proposed IFTG-HoG method. It performed better in gait individual classification as the value of average accuracy for the standard dataset 96.7%, 93.1% and 99.6%compared to HoG method by 94.1%, 85.9% and 96.2% in order. With similar motivation, we tested on Rempit datasets to recognize motorcycle rider anomaly event, and our proposed method outperforms Dalal Method.

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Section: Methodsmentioning

confidence: 99%

Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient

Rahman

Abdullah

Ariffin

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Walking patterns enhance one's step length estimation, thus increasing localization accuracy. However, one challenge of activity recognition is feature extraction [32,33].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

et al. 2023

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These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.

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“…Biometric can be defined as the science through which individual identification can be verified, established and recognized automatically based on behavioral traits [4] (speech, handwriting, and signature) or physiological features [5] [6] [7] (gait, hand vein, fingerprint, finger vein, and face). The law enforcement agencies were the ones who first used biometric systems in the 1970s to investigate criminals through the using of fingerprint recognition [8].…”

Section: Introductionmentioning

confidence: 99%

Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning

Ali

Kassim

Abdullah

2019

ijacsa

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Human identity recognition and protection of information security are current global concerns in this age of increasing information growth. Biometrics approach of defining identity is considered as one of the highly potential approaches due to its internal feature that is difficult to be artificially recreated, stolen and/or forgotten. The new recognition system based on finger vein is a unique method depending on physiological traits and parameters of the vein patterns for the human. Published works on finger vein identification have hitherto ignored the power of aggregating different types of features and classifiers in improving the performance of the biometric recognition system. In this paper, we developed a novel feature approach named as straight line approximator (SLA) for extending the feature space of vein pattern using a public data set SDUMLA-HMT comprising about 3,816 images of finger vein for 160 persons. Furthermore, we applied a set of extreme learning machine (ELM) and support vector machine (SVM) classifier in different kernels. Then, we used the combination rules to improve the performance of the system. The experiment result of the proposed method achieved an accuracy of 87% using (DS and GWAR) rules at rank 1, while the accuracy of DS rule 93% and GWAR rule 92% at rank 5.

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The analysis for Gait Energy Image based on statistical methods

Cited by 5 publications

References 13 publications

Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient

Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning

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