An overview of gesture recognition

Abdallah, Manel Ben; Kallel, M. A.; Bouhlel, Med Salim

doi:10.1109/setit.2012.6481883

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…Gestures are defined as the movement of a body part (fingers, hands, arms, head, or face) or a whole body that is made with or without the intension meaning something [171,172]. For example, the expressive and meaningful motion of fingers or hands conveys meaningful information to another human, or it can be used for interacting with a real or virtual environment (virtual reality, augmented reality).…”

Section: Gait and Gesture De-identificationmentioning

confidence: 99%

De-identification for privacy protection in multimedia content: A survey

Ribarić

Ariyaeeinia

Pavešić

2016

Signal Processing: Image Communication

152

107

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a b s t r a c tPrivacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. Deidentification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents.

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Section: Gait and Gesture De-identificationmentioning

confidence: 99%

De-identification for privacy protection in multimedia content: A survey

Ribarić

Ariyaeeinia

Pavešić

2016

Signal Processing: Image Communication

152

107

View full text Add to dashboard Cite

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“…In the processing°ow of¯ngertips detection on hand gesture, the Euclidean distance between centroid (x c , y c Þ and bottom ðx m ; y m Þ is de¯ned as (6,9) 0.390 (1,4) 0.612 (2,8) 0.323 (4, 7) 0.000 (6, 0) 0.599 (1,5) 0.670 (2,9) 0.398 (4,8) 0.216 (7,8) 0.476 (1,6) 0.979 (2, 0) 0.376 (4,9) 0.636 (7,9) 0.305 (1,7) 0.494 (3,4) 0.000 (4, 0) 0.183 (7, 0) 0.223 (1,8) 0.357 (3,5) 0.162 (5, 6) 0.314 (8,9) 0.659 (1,9) 0.649 (3,6) …”

Section: Detection Of¯ngertip Featuresmentioning

confidence: 99%

“…Therefore, the performance-based model is applied in the most hand gesture recognition systems. 1 In the performance-based model, there are several types of methods that have been proposed. The¯rst type uses gray levels on an image to build the gesture model.…”

Section: Introductionmentioning

confidence: 99%

Fingertip Detection and Gesture Recognition Based on Contour Approximation

Wang

Lin

Meng

2015

Int. J. Patt. Recogn. Artif. Intell.

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In this paper, a new recognition algorithm was developed based on the skin color model and thē ngertip structure detection to improve the hand gesture recognition accuracy based on the traditional Hu's moment features. Firstly, the geometric features and the areas of skin were adopted to segment the skin region from the background. The Douglas-Peucker (D-P) algorithm was utilized to conduct the contour approximation to get a polygonal in the process of feature detection. Then the convexity point inspections were conducted in the polygonal. Secondly, we developed two rules for locating and numbering the¯ngertips. After that, we built a seven-dimensional feature vector. Finally, the hand gesture was recognized by using the distance matching criterion. The developed recognition algorithm improved the recognition accuracy by 2.7% compared with the traditional Hu's moment features. Additionally, it has good robustness in several manners such as shifting, plane rotation and scaling.

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“…A similar kind of approach using depth sensor is given by [6] Suarez, J., & Murphy, R. R in their review paper. M., Kallel, M., & Bouhlel [7] discuss about applications of gesture recognition. Further, algorithms like Otsu's method for thresholding are used, skin detection, hand movement tracking.…”

Section: Introductionmentioning

confidence: 99%

Sign Language Recognition using CNN

Wattamwar¹

2021

IJRASET

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This research work presents a prototype system that helps to recognize hand gesture to normal people in order to communicate more effectively with the special people. Aforesaid research work focuses on the problem of gesture recognition in real time that sign language used by the community of deaf people. The problem addressed is based on Digital Image Processing using CNN (Convolutional Neural Networks), Skin Detection and Image Segmentation techniques. This system recognizes gestures of ASL (American Sign Language) including the alphabet and a subset of its words. Keywords: gesture recognition, digital image processing, CNN (Convolutional Neural Networks), image segmentation, ASL (American Sign Language), alphabet

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An overview of gesture recognition

Cited by 9 publications

References 3 publications

De-identification for privacy protection in multimedia content: A survey

De-identification for privacy protection in multimedia content: A survey

Fingertip Detection and Gesture Recognition Based on Contour Approximation

Sign Language Recognition using CNN

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