Hand gesture recognition based on dynamic Bayesian network framework

Suk, Heung-Il; Sin, Bong-Kee; Lee, Seong–Whan

doi:10.1016/j.patcog.2010.03.016

Cited by 167 publications

(71 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As future work, some other time series distances, as Dynamic Time Warping Berndt & Clifford (1994), may be used or other different approaches based on statistical method should be evaluated Suk et al (2010), Lee & Kim (1999 The methods for human identity authentication based on biometrics â€" the physiological and behavioural characteristics of a person have been evolving continuously and seen significant improvement in performance and robustness over the last few years. However, most of the systems reported perform well in controlled operating scenarios, and their performance deteriorates significantly under real world operating conditions, and far from satisfactory in terms of robustness and accuracy, vulnerability to fraud and forgery, and use of acceptable and appropriate authentication protocols.…”

Section: Discussionmentioning

confidence: 99%

Application of LCS Algorithm to Authenticate Users within Their Mobile Phone Through In-Air Signatures

Guerra-Casanova¹,

Ávila²,

Pozo³

et al. 2011

Advanced Biometric Technologies

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Application of LCS Algorithm to Authenticate Users within Their Mobile Phone Through In-Air Signatures

Guerra-Casanova¹,

Ávila²,

Pozo³

et al. 2011

Advanced Biometric Technologies

View full text Add to dashboard Cite

“…Suk et al (Suk et al, 2010) proposed the DBN model for recognition of hand gestures implementing a control of media players and slide presentations. Ten isolated one-hand and two-hand gestures are enough simple, and the authors received the recognition rate of 99.59%.…”

Section: Methods Of Dynamic Gesture Recognitionmentioning

confidence: 99%

Localization and Recognition of Dynamic Hand Gestures Based on Hierarchy of Manifold Classifiers

Favorskaya

Nosov

Popov

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case). Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset "Multi-modal Gesture Recognition Challenge 2013: Dataset and Results" including 393 dynamic hand-gestures was chosen. The proposed method yielded 84-91% recognition accuracy, in average, for restricted set of dynamic gestures.

show abstract

“…Generally, a hand gesture is used as the most intuitive way to operate or instruct an object [22][23][24]. A gesture shows its possibility as an intuitive and efficient tool for interaction between computer and human in the real world through application programs like 2D and 3D mouse, TV operation, and computer game.…”

Section: Hand Gesture Analysismentioning

confidence: 99%