On Using Gait to Enhance Frontal Face Extraction

Jung, Sung-Uk; Nixon, Mark S.

doi:10.1109/tifs.2012.2218598

Cited by 8 publications

(4 citation statements)

References 47 publications

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“…They have described the motion patterns by means of Fourier analysis. Jung et al [22] have analyzed the 3-D gait trajectory to enable face acquisition and recognition in surveillance environments. Zhang et al [23] have proposed a gait recognition system based on wearable accelerometers and portable smart devices, which overcomes the typical limitations of this kind of sensors.…”

Section: Related Workmentioning

confidence: 99%

Robust gait identification using Kinect dynamic skeleton data

Gianaria

Grangetto

2018

Multimed Tools Appl

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Gait has been recently proposed as a biometric feature that, with respect to other human characteristics, can be captured at a distance without requiring the collaboration of the observed subject. Therefore, it turns out to be a promising approach for people identification in several scenarios, e.g. access control and forensic applications. In this paper, we propose an automatic gait recognition system based on a set of features acquired using the 3D skeletal tracking provided by the popular Kinect sensor. Gait features are defined in terms of distances between selected sets of joints and their vertical and lateral sway with respect to walking direction. Moreover we do not rely on any geometrical assumptions on the position of the sensor. The effectiveness of the defined gait features is shown in the case of person identification based on supervised classification, using the principal component analysis and the support vector machine. A rich set of experiments is provided in two scenarios: a controlled identification setup and a classical video-surveillance setting, respectively. Moreover, we investigate if gait can be considered invariant over time for an individual, at least in a time interval of few years, by comparing gait samples of several subjects three years apart. Our experimental analysis shows that the proposed method is robust to acquisition settings and achieves very competitive identification accuracy with respect to the state of the art.

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Section: Related Workmentioning

confidence: 99%

Robust gait identification using Kinect dynamic skeleton data

Gianaria

Grangetto

2018

Multimed Tools Appl

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“…Argyropoulos et al [15] have used a channel coding approach for constructing a model of gait employed in their gait recognition system. Jung et al [16] have developed a system to enable face acquisition and recognition in surveillance environments, by analyzing the 3-D gait trajectory.…”

Section: Related Workmentioning

confidence: 99%

Kinect-Based Gait Analysis for People Recognition Over Time

Gianaria

Grangetto

Balossino

2017

Image Analysis and Processing - ICIAP 2017

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Gait has recently attracted a great interest in the biometric field because, contrary to other classical biometric traits such as fingerprint, iris or retina, it allows to capture samples at a distance, through inexpensive and not intrusive technologies that do not need any subject's collaboration. In spite of this advantage, such technique is still not widespread for human identification task, because it is considered not to exhibit the fundamental characteristic of being invariant in the lifetime of each individual. But is this assertion really true? In this paper we investigate if gait can be considered invariant over time for an individual, at least in a time interval of few years, by comparing gait samples of several subjects three years apart. We train a Support Vector Machine with gait samples of 10 subjects, then we employ it for recognizing the same subjects with gait samples collected three years later. In addition, we try to recognize the subjects carrying three different accessories: a shoulder bag, a backpack and a smartphone.

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“…They obtained 2.4 • of MAE on BUFT dataset. Jung and Nixon [15] mainly addressed the face recognition problem by reconstructing high-quality frontal face images from a 3D ellipsoidal head-pose model and gait information. They used region and distance based refinement of head-pose estimation by SIFT features where they obtained 2.1 • of MAE on BUFT dataset.…”

Section: Point-of-interest Based Approaches For In-plane Rotationsmentioning

confidence: 99%

“…For example, when roll of the face is estimated by eye detection, the flaws in eye detection impacts roll estimation. On the other hand, tracking based methods [15,17,21,28] tends to provide substantially less error than systems that estimate the head-pose (in our case the roll) from individual frames and temporally filter the results as indicated by Murphy-Chutorian and Trivedi [20]. However, they usually require a sequence of images and strict initialization procedures (which most of the time they assumes the detection of a frontal face).…”

Section: Introductionmentioning

confidence: 97%

In-plane face orientation estimation in still images

Danışman

Bilasco

2015

Multimed Tools Appl

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International audienceThis paper addresses a fine in-plane (roll) face orientation estimation for a perspective face analysis algorithm that requires normalized frontal faces. As most of the face analysers (e.g., gender, expression, and recognition) need frontal up-right faces, there is a clear need for the precise roll estimation, as precise face normalization has an important role in classification methods. The in-plane orientation estimation algorithm is constructed on top of regular Viola-Jones frontal face detector. When a face is detected for the first time, it is rotated with respect to the face origin to find the boundaries of the detection. Mean value of these angles is said to be the measurement of the in-plane rotation of the face. Since we only need a face detection algorithm, the proposed method can work effectively on very small sized faces where traditional landmark (eye, mouth) or planar detection based estimations fail. Experiments on controlled and unconstrained large-scale datasets (CMU Rotated, YouTube, Boston University Face Tracking, Caltech, FG-NET Aging, BioID and Manchester Talking-Face) showed that the proposed method is robust to various settings for in-plane face orientation estimation in terms of RMSE and MAE. We achieved less than ±3.5 ∘ mean absolute error for roll estimation which proves that the accuracy of the proposed method is comparable to that of the state-of-the-art tracking based approaches for the roll estimation

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On Using Gait to Enhance Frontal Face Extraction

Cited by 8 publications

References 47 publications

Robust gait identification using Kinect dynamic skeleton data

Robust gait identification using Kinect dynamic skeleton data

Kinect-Based Gait Analysis for People Recognition Over Time

In-plane face orientation estimation in still images

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