Gait Recognition Using a View Transformation Model in the Frequency Domain

Makihara, Yasushi; Sagawa, Ryusuke; Mukaigawa, Yasuhiro; Echigo, Tomio; Yagi, Yasushi

doi:10.1007/11744078_12

Cited by 305 publications

(308 citation statements)

References 16 publications

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“…In a more realistic situation, the camera view angle could be arbitary. However, we note that a number existing approaches [9], [14] have been proposed recently to address the problem of view recognition and feature transformation across view. When the view angle is unknown and differs greatly from gallery and probe sets, these methods can be readily integrated with the method proposed in this paper.…”

Section: E Discussionmentioning

confidence: 99%

Gait recognition without subject cooperation

Bashir

Xiang

Gong

2010

Pattern Recognition Letters

293

204

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Section: E Discussionmentioning

confidence: 99%

Gait recognition without subject cooperation

Bashir

Xiang

Gong

2010

Pattern Recognition Letters

293

204

View full text Add to dashboard Cite

“…Gait recognition was formulated as a shape sequence matching problem [3] or spatial-temporal frequency domain analysis [11]. Nevertheless, these approaches do not provide an effective human segmentation algorithm crucial for the success of gait recognition.…”

Section: A Related Workmentioning

confidence: 99%

Human tracking and segmentation supported by silhouette-based gait recognition

Wang

Makihara

Yagi

2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-Gait recognition has recently gained attention as an effective approach to identify individuals at a distance from a camera. Most existing gait recognition algorithms assume that people have been tracked and silhouettes have been segmented successfully. Tacking and segmentation are, however, very difficult especially for articulated objects such as human beings. Therefore, we present an integrated algorithm for tracking and segmentation supported by gait recognition. After the tracking module produces initial results consisting of bounding boxes and foreground likelihood images, the gait recognition module searches for the optimal silhouette-based gait models corresponding to the results. Then, the segmentation module tries to segment people out using the provided gait silhouette sequence as shape priors. Experiments on real video sequences show the effectiveness of the proposed approach.

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“…The Cross-View Gait Recognition method is compared with other six existing methods including Baseline [12], View rectification [5], Gait Energy Image -Canonical Correlation Analysis [7], Gait Energy Image -Singular Value Decomposition [9], Gait Energy Image -Support Vector Regression [8] and Fourier Transform -Singular Value Decomposition [19]. The better performance of the Cross-View Gait Recognition is given below in Table 4.…”

Section: Cross View Gait Recognition:-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 Using a View Transformation Model in the Frequency Domain

Cited by 305 publications

References 16 publications

Gait recognition without subject cooperation

Gait recognition without subject cooperation

Human tracking and segmentation supported by silhouette-based gait recognition

Survey of Current Trends in Human Gait Recognition Approaches.

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