Anthropometric and human gait identification using skeleton data from Kinect sensor

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2014, Pages 60-61

  Andersson, Virginia ^a   Dutra, Rafael ^a   Araújo, Ricardo ^a  

^a FEDERAL UNIVERSITY OF PELOTAS   (Brazil)

Author keywords

Anthropometry; Biometrics; Gait; Machine Learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOMETRICS; KINEMATICS; LEARNING SYSTEMS; MUSCULOSKELETAL SYSTEM; PHYSIOLOGICAL MODELS;

10-FOLD CROSS-VALIDATION; BIOMETRIC IDENTIFICATIONS; GAIT; K-NEAREST NEIGHBORS; MACHINE LEARNING MODELS; MICROSOFT KINECT SENSORS; MULTI LAYER PERCEPTRON; STATISTICAL DESCRIPTORS;

ANTHROPOMETRY;

EID: 84905641597     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2554850.2555147     Document Type: Conference Paper

References (5)

1. R. Araújo, G. Graña and V. Andersson, "Towards skeleton biometric identification using the Microsoft Kinect Sensor," ACM Symposium on applied computing 2013.
2. J. H. Yoo and M. S. Nixon, "Automated markless analysis of human gait motion for recognition and classification," in ETRI, Vol. 33, no. 2, pp. 259-266, 2011.
3. D. Cunado, M. S. Nixon and J. N. Carter "Automatic extraction and description of human gait models for recognition purposes," Elsevier Computer Vision and Image Understanding, Vol. 90, no. 1, pp 1-44, 2003.
4. M. Hofmann, S. Bachman and G. Rigoll, "2.5D Gait Biometrics using the Depth Gradient Histogram Energy Image," BTAS IEEE Fifth International Conference pp. 399-403, 2012.
5. B. C. Munsell et al, "Person identification using full-body motion and anthropometric biometrics from kinect videos," ECCV 2012. pp. 91-100, 2012.