A dynamic gesture recognition and prediction system using the convexity approach

Barros, Pablo; Maciel-Junior, Nestor T.; Fernandes, Bruno; Bezerra, Byron Leite Dantas; Fernandes, Stênio

doi:10.1016/j.cviu.2016.10.006

Cited by 42 publications

(20 citation statements)

References 17 publications

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“…Since dynamic gesture recognition is not able to peform on hand shapes, it needs to combine with motion, direction, and trajectory. The advantages of proposed method are not dependent on the context and using simple backgrounds comparing with other methods [5] [6]. The details of the method will be presented in the following section.…”

Section: • Processing Timementioning

confidence: 99%

“…Step 2: Designing training sample Several training samples have been used such as MSRGesture3D, Cambridge-Gesture [6], 20bn-jester [9]. Each of them is built for a specific application based on types of gestures.…”

Section: Step 1: Processing Imagementioning

confidence: 99%

“…Although the accuracy of the method is quite high, user is dependent on characteristics of sample data. The authors in [6] used the convexity technique described as a technique of extracting all points surrounding a hand to find minimum set of points. After extracting feature vectors, the author uses hidden Markov model (HMM).…”

Section: Introductionmentioning

confidence: 99%

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An ANN-based gesture recognition algorithm for smart-home applications

2020

KSII TIIS

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The goal of this paper is to analyze and build an algorithm to recognize hand gestures applying to smart home applications. The proposed algorithm uses image processing techniques combing with artificial neural network (ANN) approaches to help users interact with computers by common gestures. We use five types of gestures, namely those for Stop, Forward, Backward, Turn Left, and Turn Right. Users will control devices through a camera connected to computers. The algorithm will analyze gestures and take actions to perform appropriate action according to users requests via their gestures. The results show that the average accuracy of proposal algorithm is 92.6 percent for images and more than 91 percent for video, which both satisfy performance requirements for real-world application, specifically for smart home services. The processing time is approximately 0.098 second with 10 frames/sec datasets. However, accuracy rate still depends on the number of training images (video) and their resolution.

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Section: • Processing Timementioning

confidence: 99%

Section: Step 1: Processing Imagementioning

confidence: 99%

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An ANN-based gesture recognition algorithm for smart-home applications

2020

KSII TIIS

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“…In literature [10], twelve Fourier descriptors are used as feature vectors for 10 gesture types. In literature [11] uses the pixel histogram to represent the relationship between the number of fingers and it's corresponding, and distinguish the 1-9 gesture, the average recognition rate is about 90%. The literature [12] detected the outline information of the finger and judged the category by its specific number and direction.…”

Section: Related Workmentioning

confidence: 99%

“…Since the threshold area contains a large number of non-skinned pixels, there are many misjudgment areas in the segmentation map [11]. The Gaussian model uses the Gaussian probability density function to count the probability of a pixel in the skin tone.…”

Section: Fig 2 Skin Color Segmentation Processmentioning

confidence: 99%

Jointly network: a network based on CNN and RBM for gesture recognition

Cheng

Sun

et al. 2018

Neural Comput & Applic

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Hand belongs to non-rigid objects and is rich in variety, making gesture recognition more difficult. The essence of dynamic gesture recognition is the classification and recognition of single-frame still images. Therefore, this paper mainly focuses on static gesture recognition. At present, there are some problems in gesture recognition, such as accuracy, real-time or poor robustness. To solve the above problems, in this paper, the Kinect sensor is used to obtain the color and depth gesture samples, and the gesture samples are processed. On this basis, a jointly network of CNN and RBM is proposed for gesture recognition. It mainly uses superposed network of multiple RBMs to carry out unsupervised feature extraction and combined with supervised feature extraction of CNN. Finally, these two features are combined to classify them. The simulation results show that the proposed jointly network has a better performance in identifying simple background gesture samples, and the recognition capability of gesture samples in complex background needs to be improved.

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State of the art and perspectives on traditional and emerging biometrics: A survey

Traoré

Alshahrani

Obaidat

2018

Security and Privacy

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The last three decades have seen a shift and impressive progress in the biometric technologies landscape. Several major real‐world applications of biometrics have been released and are currently being used around the world. At the same time, several new biometrics modalities have emerged and have started making an impact in securing sensitive data, information, and systems. We present in this paper a survey of traditional and emerging biometrics by emphasizing their connection to human body parts, behavior, and cognition. A review of the state of the art in the key technologies is carried out along with a discussion of major applications currently available in the real world.

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A dynamic gesture recognition and prediction system using the convexity approach

Cited by 42 publications

References 17 publications

An ANN-based gesture recognition algorithm for smart-home applications

An ANN-based gesture recognition algorithm for smart-home applications

Jointly network: a network based on CNN and RBM for gesture recognition

State of the art and perspectives on traditional and emerging biometrics: A survey

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