Frontal View Gait Recognition With Fusion of Depth Features From a Time of Flight Camera

Zulcaffle, Tengku Mohd Afendi; Kurugöllü, Fatih; Crookes, Danny; Bouridane, Ahmed; Farid, Mohsen

doi:10.1109/tifs.2018.2870594

Cited by 20 publications

(13 citation statements)

References 55 publications

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“…• Microprocessor: Intel® Core™ i3-4030U CPU The computational cost analysis of the algorithms is based on the evaluation made by Tengku et al [35]. The algorithms are run 10 times and their maximum, minimum, standard deviations and averages are recorded.…”

Section: Time Complexity Of Algorithmsmentioning

confidence: 99%

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Entuni¹,

Zulcaffle²,

Kipli³

2020

j.asep

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Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and compared with the previous method which are K-Means and Otsu’s thresholding. The best severity estimation algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This algorithm is more effective than other algorithms in terms of not only better segmentation performance but also low time complexity that is 34.75s in average with 0.2697s standard deviation.

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Section: Time Complexity Of Algorithmsmentioning

confidence: 99%

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Entuni¹,

Zulcaffle²,

Kipli³

2020

j.asep

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show abstract

“…El-Alfy et al [15] proposed a personal authentication method that captured the geometric properties of silhouette boundaries in an image by evaluating the contour curvature using gauss maps. Zulcaffle et al [16] presented a method that used images acquired by a 3D ToF camera. They extracted the silhouette of a person from the depth image and used multiple classifiers to identify the person, which switched the algorithm depending on the package's existence and the walking intensity.…”

Section: Background and Related Workmentioning

confidence: 99%

Privacy-Aware Gait Identification With Ultralow-Dimensional Data Using a Distance Sensor

2023

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As one of the most natural user behaviors, walking has been widely focused on developing personal identification systems due to its unique biometric authentication features. Popular visual solutions are usually affected by various environmental conditions, and their redundant user information (e.g., body type and appearance) makes it more challenging for users to maintain privacy and security. This paper proposes a distance sensor-based gait identification system that uses only one-dimensional data with a simple system structure. Specifically, a time-of-flight sensor was placed in front of a walking person, and a time series of distances was acquired. We extracted gait features from the data by calculating the velocity and acceleration curves and identifying individuals using a random forest classifier. We evaluated our system on 10 users using leave-one-out cross-validation. The average identification accuracy was 91.05% for 10 users. This study shows that gait recognition is possible using only one-dimensional time-series data with a noncontact sensor. It can be used as a contactless identification, reducing the computational resources required for low-cost and low-power consumption edge computing.

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“…11,12 In reality, however, these methods rely heavily on the data collection area and require the camera to be placed at a certain height or distance to capture full gait sequences, which are only applicable in outdoor or wide indoor spaces, and not in applications such as a secure narrow corridor. 2,13 Attempts to resolve this dilemma have resulted in the development of frontal-view gait recognition methods.…”

Section: Introductionmentioning

confidence: 99%

“…Despite that much progress has been made for gait recognition, so far, most of the existing works only focus on the lateral-view gait recognition, since more spatial and temporal information can be observed in the sagittal plane 11 , 12 . In reality, however, these methods rely heavily on the data collection area and require the camera to be placed at a certain height or distance to capture full gait sequences, which are only applicable in outdoor or wide indoor spaces, and not in applications such as a secure narrow corridor 2 , 13 . Attempts to resolve this dilemma have resulted in the development of frontal-view gait recognition methods.…”

Section: Introductionmentioning

confidence: 99%

Frontal-view gait recognition using discriminative dynamics feature representations and learning

Deng,

Zou,

Zhu

et al. 2024

J. Electron. Imag.

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Gait recognition plays an important role in the area of biometric recognition. Despite that much progress has been made for gait recognition in recent years, most of them are based on lateral-view gait characteristics. These methods usually require a large data collection area to capture full gait sequences, which are only applicable in wide outdoor spaces. In this paper, we propose a new frontal-view method for human gait recognition based on gait dynamics and deep transfer learning. The frontal human silhouettes are characterized with four kinds of time-varying gait features, namely the lower limb length ratio, lower limb area ratio, swing area of lower limb, and swing angle of lower limb. In the training phase, gait dynamics underlying the aforementioned time-varying gait features is approximated by radial basis function neural networks through deterministic learning theory. This kind of dynamics information reflects the essential dynamical attribute of human walking and is insensitive to the deformation of gait silhouettes caused by frontal-view walking process. To further improve the recognition rates, the extracted gait dynamics and deep transfer learning technique are incorporated, and a deep transfer learning based feature fusion strategy are investigated for human identification task in the recognition phase. To further improve the robustness against different walking conditions, three kinds of gait representations are fused to provide a comprehensive characterization of human walking movement. Finally, comprehensive experiments are carried out on the well-known public gait databases to demonstrate the recognition performance of the proposed algorithm.

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Frontal View Gait Recognition With Fusion of Depth Features From a Time of Flight Camera

Cited by 20 publications

References 55 publications

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Privacy-Aware Gait Identification With Ultralow-Dimensional Data Using a Distance Sensor

Frontal-view gait recognition using discriminative dynamics feature representations and learning

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