Hand gesture recognition: An overview

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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“…To date, there have only been a few attempts to develop biometric verification systems based on hand-gesture recognition [173,174,176,177].…”

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

145

107

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“…Yang et al [2] define that, HMM is considered as one of the most popular approaches for dynamic hand gesture recognition. In HMM, hidden factors ensure different states and variants from these state transitions are visible.…”

Section: Richarz and Finkmentioning

confidence: 99%

“…In hand gesture recognition system (GRS), hand gestures can be a continuous gesture referred as dynamic gestures or can be fixed or single gesture referred as static gestures [2].…”

Section: Introductionmentioning

confidence: 99%

“…In visionbased approach, algorithms are used to derive hand pose and trajectory information and environmental factors such as background illumination, hands occlusion, and skin color plays a significant role during the recognition process. In non-vision-based scheme, users' needs to wear data gloves and handle the cabling part of recognizing system [2].…”

Section: Introductionmentioning

confidence: 99%

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Study of Hand Gesture Recognition and Classification

Srinivas

Rajagopal

2017

Asian J Pharm Clin Res

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To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications. Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration.

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“…In 2007, Premaratne et al at the University of Wollongong demonstrated a real-time working prototype that could interact with a number of electronic devices via hand gestures [1,3,[4][5][6][7][8][9][10][11][12]. There are significant obstacles that has stagnated the accuracy of hand gesture recognition which has confined the number of gestures suitable for communication to very few (less than 30) due to the difficulty in accurately recognizing hand gestures by different users.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Framework

Premaratne

Yang

Zhou³

et al. 2014

Intelligent Computing Methodologies

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Abstract. Sign languages originated long before any speech-based languages evolved in the world. They contain subtleties that rival any speech-based languages conveying a rich source of information much faster than any speechbased languages. Similar to the diversity of speech-based languages, sign languages vary from region to region. However, unlike the speech counterpart, sign languages from diverse regions from the world have much common traits that originate from human evolution. Researchers have been intrigued by these common traits and have always wondered whether sign language-type communication is possible for instructing the computers opposed to the mundane keyboard and mouse. This trend is popularly known as Human Computer Interaction (HCI) and has used a subset of common sign language hand gestures to interact with machines through computer vision.Since the sign languages comprise of thousands of subtle gestures, a new sophisticated approach has to be initiated for eventual recognition of vast number of gestures. Hand gestures comprise of both static postures and dynamic gestures and can carry significantly rich vocabulary describing words in the thousands. In this article, we present our latest research that describes a mechanism to accurately interpret dynamic hand gestures using a concept known as 'gesture primitives' where each dynamic gesture is described as a collection of many primitives over time that can drive a classification strategy based on Hidden Markov Model to reliably predict the gesture using statistical knowledge of such gestures. We believe that even though our work is in its infancy, this strategy can be extended to thousands of dynamic gestures used in sign language to be interpreted by machines.

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Hand gesture recognition: An overview

Cited by 27 publications

References 22 publications

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

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

Study of Hand Gesture Recognition and Classification

Dynamic Hand Gesture Recognition Framework

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