Rule-based trajectory segmentation for modeling hand motion trajectory

Beh, Jounghoon; Han, David K.; Ko, Hanseok

doi:10.1016/j.patcog.2013.11.010

Cited by 35 publications

(21 citation statements)

References 34 publications

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“…Some of them apply image analysis to track the hand and gesture motions from video footage [5,26,32]. In this paper we do not seek to advance the field of motion tracking.…”

Section: Related Workmentioning

confidence: 99%

Cracking Android Pattern Lock in Five Attempts

Ye¹,

Tang²,

Fang³

et al. 2017

Proceedings 2017 Network and Distributed System Security Symposium

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Abstract-Pattern lock is widely used as a mechanism for authentication and authorization on Android devices. This paper presents a novel video-based attack to reconstruct Android lock patterns from video footage filmed using a mobile phone camera. Unlike prior attacks on pattern lock, our approach does not require the video to capture any content displayed on the screen. Instead, we employ a computer vision algorithm to track the fingertip movements to infer the pattern. Using the geometry information extracted from the tracked fingertip motions, our approach is able to accurately identify a small number of (often one) candidate patterns to be tested by an adversary. We thoroughly evaluated our approach using 120 unique patterns collected from 215 independent users, by applying it to reconstruct patterns from video footage filmed using smartphone cameras. Experimental results show that our approach can break over 95% of the patterns in five attempts before the device is automatically locked by the Android operating system. We discovered that, in contrast to many people's belief, complex patterns do not offer stronger protection under our attacking scenarios. This is demonstrated by the fact that we are able to break all but one complex patterns (with a 97.5% success rate) as opposed to 60% of the simple patterns in the first attempt. Since our threat model is common in day-to-day life, this paper calls for the community to revisit the risks of using Android pattern lock to protect sensitive information.

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“…Some of them apply image analysis to track the hand and gesture motions from video footage [5,26,32]. In this paper we do not seek to advance the field of motion tracking.…”

Section: Related Workmentioning

confidence: 99%

Cracking Android Pattern Lock in Five Attempts

Ye¹,

Tang²,

Fang³

et al. 2017

Proceedings 2017 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

show abstract

“…Besides, more discriminative features were also proposed by projecting the motion trajectory to the higher-dimensional feature space [143]. Beh et al [144] further composed the hand motion trajectory as a unique series of straight and curved segments. They proposed an automated process of segmenting gesture trajectories based on a simple set of threshold values in the angular change measure.…”

Section: A Feature Representationmentioning

confidence: 99%

Survey on 3D Hand Gesture Recognition

Cheng

Yang

Liu

2016

IEEE Trans. Circuits Syst. Video Technol.

290

110

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3D hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public datasets which are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 3D hand modeling, static hand gesture recognition, hand trajectory gesture recognition and continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.

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“…In paper [4] authors propose a method of modeling hand gestures based on the angles and angular change rates of the hand trajectories. Each hand motion trajectory is composed of a unique series of straight and curved segments.…”

Section: State Of the Art On Application Of Hmm To Gestures And Actiomentioning

confidence: 99%

“…Those joints are used to calculate angle features of left and right arm. Afterward, the gesture feature vector is clustered using the k-means clustering algorithm [4], the k is set as 16.…”

Section: State Of the Art On Application Of Hmm To Gestures And Actiomentioning

confidence: 99%

Human actions recognition on multimedia hardware using angle-based and coordinate-based features and multivariate continuous hidden Markov model classifier

Hachaj

Ogiela

2015

Multimed Tools Appl

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In this paper we have proposed human actions recognition schema that uses angle-based and state-of-the-art coordinates-based features and multivariate continuous hidden Markov model (HMM) classifier with Gaussian distribution. The main novelty of this paper besides presenting this approach is its evaluation on large (containing 770 actions) motion-capture dataset of various gym exercises. We have evaluated HMM with various number of hidden states and Gaussian mixture model classifier. We have performed PCA analysis of both features sets to justify our choices. We have also test 24 subsets of proposed angle-based features dataset in order to estimate which body joints are vital for correct actions recognition. The highest recognition rate in k-fold cross validation was 97±14 % and was obtained for 4-states HMM with 10 angle-based features. The common problem with markerless motion capture vision -based systems is that if some parts of body surface are covered by another part it is impossible to perform accurate features measurements. Knowing this we have evaluated 24 subsets of proposed features dataset in order to estimate which body joints are vital for correct actions recognition.

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Rule-based trajectory segmentation for modeling hand motion trajectory

Cited by 35 publications

References 34 publications

Cracking Android Pattern Lock in Five Attempts

Cracking Android Pattern Lock in Five Attempts

Survey on 3D Hand Gesture Recognition

Human actions recognition on multimedia hardware using angle-based and coordinate-based features and multivariate continuous hidden Markov model classifier

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