On using gait biometrics to enhance face pose estimation

Jung, Sung-Uk; Nixon, Mark S.

doi:10.1109/btas.2010.5634473

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…The periodic factor is proportional to walking position; the scaling factor when a person walks toward camera. In [14], we demonstrated this looming effect when a person walks towards camera. However, given the camera projection matrix this looming effect can be removed by 3D analysis.…”

Section: Homogeneous Matrix Calculationmentioning

confidence: 93%

“…The gait trajectory in 2D space contains a non-linear factor; the looming effect [14] which can affect the accuracy of the gait trajectory. Therefore, the domain is changed from the 2D image plane into 3D space using the camera projection matrix because in 3D space there are many advantages such that the height of walking person is constant and the looming effect can be ignored.…”

Section: Trajectory Calculation In 3d Spacementioning

confidence: 99%

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Estimation of 3D head region using gait motion for surveillance video

Jung¹,

Nixon²

2011

4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011)

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Detecting and recognizing people is important in surveillance. Many detection approaches use local information, such as pattern and colour, which can lead to constraints on application such as changes in illumination, low resolution, and camera view point. In this paper we propose a novel method for estimating the 3D head region based on analysing the gait motion derived from the video provided by a single camera. Generally, when a person walks there is known head movement in the vertical direction, regardless of the walking direction. Using this characteristic the gait period is detected using wavelet decomposition and the heel strike position is calculated in 3D space. Then, a 3D gait trajectory model is constructed by non-linear optimization. We evaluate our new approach using the CAVIAR database and show that we can indeed determine the head region to good effect. The contributions of this research include the first use of detecting a face region by using human gait and which has fewer application constraints than many previous approaches.

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Section: Homogeneous Matrix Calculationmentioning

confidence: 93%

Section: Trajectory Calculation In 3d Spacementioning

confidence: 99%

Estimation of 3D head region using gait motion for surveillance video

Jung¹,

Nixon²

2011

4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011)

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“…Unlike other methods, our new method can be more robust under translation since our method is based on local feature matching first, and then analyzing the pattern of the face image. In our previous work [30], we presented another experiment at low frame rate ( 10 fps).…”

Section: Table I Comparison With Previous Methodsmentioning

confidence: 99%

“…This paper is an extended version of our previous research [30], [31]. We now analyze the 3-D face model in more detail and the extraction of the gait characteristics, together with performance comparison with other techniques.…”

Section: Introductionmentioning

confidence: 99%

On Using Gait to Enhance Frontal Face Extraction

Jung

Nixon

2012

IEEE Trans.Inform.Forensic Secur.

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Abstract-Visual surveillance finds increasing deployment for monitoring urban environments. Operators need to be able to determine identity from surveillance images and often use face recognition for this purpose. In surveillance environments, it is necessary to handle pose variation of the human head, low frame rate, and low resolution input images. We describe the first use of gait to enable face acquisition and recognition, by analysis of 3-D head motion and gait trajectory, with super-resolution analysis. We use region-and distance-based refinement of head pose estimation. We develop a direct mapping to relate the 2-D image with a 3-D model. In gait trajectory analysis, we model the looming effect so as to obtain the correct face region. Based on head position and the gait trajectory, we can reconstruct high-quality frontal face images which are demonstrated to be suitable for face recognition. The contributions of this research include the construction of a 3-D model for pose estimation from planar imagery and the first use of gait information to enhance the face extraction process allowing for deployment in surveillance scenarios.

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“…As mentioned before, the frame in which the heel strike takes place can be extracted through the gait motion even when the foot is hidden by another leg and the image quality is low. In this research, the gait trajectory model [10] is deployed. This model is applied to detect the key frame in which the heel strikes happen.…”

Section: Introductionmentioning

confidence: 99%

Detection Human Motion with Heel Strikes for Surveillance Analysis

Jung

Nixon

2011

Computer Analysis of Images and Patterns

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Abstract. Heel strike detection is an important cue for human gait recognition and detection in visual surveillance since the heel strike position can be used to derive the gait periodicity, stride and step length. We propose a novel method for heel strike detection using a gait trajectory model, which is robust to occlusion, camera view and to low resolution which can generalize to a variety of surveillance imagery. When a person walks, the movement of the head is conspicuous and sinusoidal. The highest point of the trajectory of the head occurs when the feet cross. Our gait trajectory model is constructed from trajectory data using non-linear optimization. Then, the key frames in which the heel strike takes place are extracted. A Region Of Interest (ROI) is extracted using the silhouette image of the key frame as a filter. Finally, gradient descent is applied to detect maxima which are considered to be the time of the heel strikes. The experimental results show a detection rate of 95% on two databases. The contribution of this research is the first use of the gait trajectory in the heel strike position estimation process and we contend that the approach is a new approach for basic analysis in surveillance imagery.

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On using gait biometrics to enhance face pose estimation

Cited by 9 publications

References 20 publications

Estimation of 3D head region using gait motion for surveillance video

Estimation of 3D head region using gait motion for surveillance video

On Using Gait to Enhance Frontal Face Extraction

Detection Human Motion with Heel Strikes for Surveillance Analysis

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