Automatic extraction and description of human gait models for recognition purposes

By D. Cunado, Mark S. Nixon, J. Carter. Computer Vision and Image Understanding, 2003

457 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/585e5e6a2ab846198510d8bac5321129b97fc7a1

How researchers see this paper

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

In Artificial Intelligence

1. Dacheng Tao
2. Dong Xu
3. Shuicheng Yan
4. Xuelong Li
5. S. Maybank
6. Xindong Wu
7. K. Plataniotis
8. Yasushi Makihara

In Biomedical Engineering

1. Dacheng Tao
2. Yasushi Makihara
3. Yasushi Yagi
4. Tomio Echigo
5. Torkjel Søndrol
6. Davrondzhon Gafurov
7. Kirsi Helkala
8. Xuelong Li

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Xuelong Li
3. Shuicheng Yan
4. Luc Van Gool
5. Trevor Darrell
6. Ling Shao
7. Yi Yang
8. Dong Xu

Citations in the corpus

• Gait recognition: a challenging signal processing technology for biometric identification (2005, methodology): “In [40], a model-based feature analysis method was presented for the automatic extraction and description of human gait for recognition.”
• Fusion of static and dynamic body biometrics for gait recognition (2003, unknown): “Due to the difficulties of automatic parameter recovery from video, few methods except [14] and [26] used higher level information, e.”
• Biometric Gait Authentication Using Accelerometer Sensor (2006, background): “Barclay et al. [13] showed that humans can also recognize the gender of the walker from MLD. Earlier studies on visionbased gait recognition showed promising results, usually with small sample sizes [14], [15], [16], [17], [18], [19].”
• General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (2007, background): “…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 parameter-based models [24], [8].”
• A review of biometric technology along with trends and prospects (2014, unknown): “Table 4: Summary of behavioral modalities: Modality Feature Set Recognition Techniques Datasets Voice Spectrum, Glottal pulse features, pitch, energy, duration, rhythm, temporal features, phones, idiolects, semantics, accent, pronunciation, LPCC [204], MFCC [205], VQ [206-208], HMM [209], GMM [210, ”
• 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.”
• Gait Recognition Using a View Transformation Model in the Frequency Domain (2006, methodology): “Some methods [1][2] extracted periodical features of leg motion by Fourier analysis.”