A Real-Time Hand Gesture Recognition Method

Fang, Yikai; Wang, Kongqiao; Cheng, Jian; Lu, Hanqing

doi:10.1109/icme.2007.4284820

Cited by 162 publications

(69 citation statements)

References 8 publications

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“…For each input image of 320 × 480 pixels, we use sliding window to extract the Adaptive HOG-LBP feature and put it into the SVM classifier. In order to ensure the accuracy of detection, feature extraction and classification go on in multi-scale [10]. In addition, we use the candidate window to accelerate the speed of detection, in order to achieve real-time tracking requirements.…”

Section: Adaptive Hog-lbp Detector For Palm Trackingmentioning

confidence: 99%

Adaptive HOG-LBP Based Learning for Palm Tracking

Gao¹,

Cao²

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-Hand tracking is a challenging research direction in computer vision field. Although gesture tracking algorithm has been widely applied to the system of human-computer interaction, it is difficult to meet the robustness and the realtime requirements because of the hand lacks sufficiently rich texture information for discrimination. In this paper, we propose a new method named Adaptive HOG-LBP features to track the palm in the unfettered color images by fusing the HOG features and LBP features. The fused features could give more texture information of the edge features and substructure of the palm, and are less sensitive to light variations and background clusters. Macro structural features of the palm contain the maximum amount of information that can be used for discrimination, so we can use the fusion of HOG features and LBP features to process palm detection. The fusion features are inputted into linear SVM classifier learning. Experimental results show that in our own established palm dataset, performance has been improved significantly. Particularly worthy of note is that in our own established database, most of gesture pictures are of low quality, and the palm library contains a wealth of inter-plane and out-of-plane rotated pictures.

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Section: Adaptive Hog-lbp Detector For Palm Trackingmentioning

confidence: 99%

Adaptive HOG-LBP Based Learning for Palm Tracking

Gao¹,

Cao²

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Section: A Hand Detectionmentioning

confidence: 99%

“…Approaches using shape models require sufficient contrast between object and background [6]. Considering tradeoff between computational cost and accuracy of detection, we use pixel based non parametric method under YCbCr color space [6]. The importance of YCbCr color model relies on two main aspects [15]; the Y component is separated from the Cb and Cr which are the chrominance components, and secondly these chrominance components depend on how human perceive this color spectrum.…”

Section: A Hand Detectionmentioning

confidence: 99%

Hand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM Approach

Darwish¹,

Madbouly²,

Khorsheed³

2016

IJET

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Abstract-Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Hidden Markov models (HMM's) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. In this paper several results concerning static hand gesture recognition using an algorithm based on Type-2 Fuzzy HMM (T2FHMM) are presented. The features used as observables in the training as well as in the recognition phases are based on Singular Value Decomposition (SVD) that optimally exposes the geometric structure of a matrix. SVD is an extension of Eigen decomposition to suit non-square matrices to reduce multi-attribute hand gesture data to feature vectors. In our approach, we replace the basic HMM arithmetic operators by some adequate Type-2 fuzzy operators that permits us to relax the additively constraint of probability measures. Therefore, T2FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. Experimental results show that T2FHMMs can effectively handle noise and dialect uncertainties in hand signals besides a better classification performance than the classical HMMs. The recognition rate of the proposed system is 100% for uniform hand images and 95.5% for cluttered hand images.Index Terms-Hand gesture recognition, sign language, type-2 fuzzy logic, hidden markov model.

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“…The other drawback was the limited number of hand gestures .If any gesture apart from fixed number of gesture was performed, then it becomes difficult for the camera to identify that gesture. [2,6,7] These problems were solved by augmented reality concept .Even if there are same colors in the background and foreground; it hardly matters [8]. Hand gestures are not limited in number.…”

Section: Introductionmentioning

confidence: 99%

Smart Device Control Using Augmented Reality

R.K¹,

Shukla²,

Kolhe³

et al. 2015

International Journal of Innovative Research in Electrical, Ele

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Abstract:In recent years, augmented reality technology is being widely used in various areas. Augmented Reality (AR) is basically a real view modified by a computer. It uses computer generated imagery and sounds to enhance one's current perception of reality. While virtual reality describes an imaginary world that exists in computers and our minds, augmented reality is a mixture of real world and virtual world. It is therefore different for the human mind to tell the difference between real world and augmented reality. In this paper, we describe the development of a prototype that uses augmented reality to control electrical devices like fan, motors, AC etc by hand gestures. Up to 8 electrical devices can be controlled. A virtual plane is present between user and display. The user interacts with this virtual plane and hands and fingers are tracked and gestures recognized. These gestures are then used to select the button on the display which are used for selecting a particular device and to turn it on or off as per the requirement of user. This project is very helpful for people of physical limitations like old age people, physically disabled people and also used for industrial and office work.

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A Real-Time Hand Gesture Recognition Method

Cited by 162 publications

References 8 publications

Adaptive HOG-LBP Based Learning for Palm Tracking

Adaptive HOG-LBP Based Learning for Palm Tracking

Hand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM Approach

Smart Device Control Using Augmented Reality

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