Gait analysis for recognition and classification

By L. Lee, W. Grimson. Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, 2002

852 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/68d885a2de9217ba787b8c8e55089944bbc558ee

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

A Study on Gait-Based Gender Classification (2009, methodology): “In [6], Lee and Grimson employ features from moving human silhouettes for gender classification.”
Micro-Doppler effect in radar: phenomenon, model, and simulation study (2006, neutral): “Various computer vision and ultrasound techniques have been developed to measure gait parameters [23—28].”
Individual recognition using gait energy image (2006, background): “In Lee and Grimson’s work [11], human silhouette is divided into local regions corresponding to different human body parts, and ellipses are fitted to each region to represent the human structure.”
Automatic gait recognition based on statistical shape analysis (2003, methodology): “[36], [41], [45], [47], [52], so it is inevitably affected.”
Silhouette Analysis-Based Gait Recognition for Human Identification (2003, methodology): “In summary, Comparison 1 shows that the proposed method outperforms [11], [22], [36] and is comparable to [34] in recognition rate, but is the lowest in computational cost.”
A survey on visual surveillance of object motion and behaviors (2004, background): “Statistical recognition techniques usually characterize the statistical description of motion image sets, and have been well developed in automatic gait recognition [60], [163]–[168], [182], [184].”
Fusion of static and dynamic body biometrics for gait recognition (2003, neutral): “Current gait recognition approaches may be explicitly classified into two major categories, namely model-based methods [2,4,10] and motion-based methods [7,8,11,12].”
Identification of humans using gait (2004, methodology): “Besides this, the use of the width feature imparts uniformity to feature representation across different individuals.”
Gait recognition: a challenging signal processing technology for biometric identification (2005, methodology): “This is why there are methods that use only the magnitude and phase of the Fourier transform at the fundamental walking frequency [30], [44].”
Social signal processing: Survey of an emerging domain (2009, background): “Like in the case of gestures, machine recognition of walking style (or gait) has been investigated as well, but only for purposes different from SSP, namely recognition and identification in biometric applications [100][102][206].”
Support vector machines for automated gait classification (2005, methodology): “In a gender classification task using video sequence images, Lee & Grimson [12] also reported superior SVM performance with Linear kernel compared to a Polynomial kernel.”
Pattern Recognition (2018, neutral): “In [22] , Lee and Grimson employed features from moving human silhouettes for gender clas-siﬁcation.”
Gait Components and Their Application to Gender Recognition (2008, background): “Many other efforts have also been spent on gait information processing [2], [4], [6], [9], [10], [12], [19], [22], [38], and since then, gait silhouette information has been widely utilized, e.”
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (2007, methodology): “Currently, there are several successful representation models such as appearance-based models [6], [16], [19], [22], [29], [30], [34], [37], [40], stochastic statistical models [19], articulated biomechanics models [36], [3], [1], [7] in which a set of parameters describes the gait, and other…”
The humanID gait challenge problem: data sets, performance, and analysis (2005, background): “It is only recently that human identification (HumanID ) from gait has received attention and become an active area of computer vision [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29].”
Improved gait recognition by gait dynamics normalization (2006, background): “The first stage is the extraction of features such as whole silhouettes [8], [3], principal components of silhouette boundary vector variations [11], silhouette width vectors [6], preshape representation [19], silhouette parts [20], [7], or Fourier descriptors [12].”