Dense motion analysis of German finger spellings

Tanwar, Vishesh Kumar; Buckchash, Himanshu; Raman, Balasubramanian; Bhargava, Rama

doi:10.1007/s11042-018-6533-0

“…These participants utilized variants of D-CNNs frameworks in their end-to-end SLR methods to achieve the recognition accuracies in the range 85.49-99% whereas P2SLR reported 90.76-98.09%. For the LSA64 dataset, we consider the features based SLR methods proposed by Ronchetti et al [41] and Tanwar et al [1] with reported accuracies as 95.95% and 82.57% respectively. Ronchetti's method constituted the hand tracking and segmentation of colored gloves, followed by the Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM) based classifier.…”

Section: Comparison With State-of-the-art Slr Methodsmentioning

confidence: 99%

“…To reduce the computational complexity without much compromising the security efficiency of the proposed image encryption scheme as a cloud-service, it is sufficient to obfuscate the least-four bit-planes information (as they contain minimal amount of information) by location-wise adding random-noise say n 1 3 , n for j ← 1 to BP cols do 8:…”

Section: ) Generation Of Noise-vectorsmentioning

confidence: 99%

“…For instance, Argentine SL is used in Argentina, German SL in Germany and Belgium and, American SL in the United States of America. For decades, numerous feature-based SLR methods have been proposed [1] [2], and recently, the D-CNNs based SLR frameworks have achieved remarkable recognition performance [3] [4].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we address an SL individual's security and privacy concerns, which may occur with the leakage of visual information of the individual while using cloud-services for an automatic SLR system. Protecting the SL users' visual information from an adversary (present at CSP), such as face and background environmental details, before transmitting SL data to CSP, is a primary requisite as the leakage of SL user's information may increase the risk of violence with them 1 . For instance, an adversary may misuse this visual information for a security breach to curate illegal activities against SL individuals, such as kidnapping and human trafficking.…”

Section: Introductionmentioning

confidence: 99%

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P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures

Tanwar¹,

Sharma²,

Raman³

et al. 2022

Preprint

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<div>Cloud-based services have revolutionized data storage and processing tasks. However, these services raise security concerns as service providers may misuse the user’s stored data. Privacy loss is particularly problematic for hearing and speech impaired individuals that may need to use cloud infrastructure for sign language recognition (SLR). Addressing these challenges, this paper presents a privacy-preserving sign language recognition (P2SLR) as a cloud-service that operates over cloud infrastructure without revealing the individual’s visual information to the cloud service provider (CSP). The proposed P2SLR system is realized through two innovations: (a) block-based probabilistic image encryption scheme that combines fractional-order chaotic system (FOCS) and singular value decomposition (SVD) to obfuscate the visual information in video frames, and (b) a cloud-residing deep convolutional neural network (DCNN) based recognition architecture with a modified classifier to recognize gestures from encrypted video. The proposed scheme is validated for American, Argentinian, and German sign languages. The proposed scheme achieved recognition accuracy in the range 90:76 - 98:09%, comparable to existing state-of-the-art SLR techniques in the plain domain (PD). The proposed image encryption scheme is secure under standard cryptographic image attacks, protecting the individual’s identity. P2SLR is the first move towards developing a secure SLR system over the cloud.</div>

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“…For instance, Argentine SL is used in Argentina, German SL in Germany and Belgium and, American SL in the United States of America. For decades, numerous feature-based SLR methods have been proposed [1] [2], and recently, the D-CNNs based SLR frameworks have achieved remarkable recognition performance [3] [4].…”

Section: Introductionmentioning

confidence: 99%

P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures

Tanwar¹,

Sharma²,

Raman³

et al. 2022

Preprint

Self Cite

0

View full text Add to dashboard Cite

<div>Cloud-based services have revolutionized data storage and processing tasks. However, these services raise security concerns as service providers may misuse the user’s stored data. Privacy loss is particularly problematic for hearing and speech impaired individuals that may need to use cloud infrastructure for sign language recognition (SLR). Addressing these challenges, this paper presents a privacy-preserving sign language recognition (P2SLR) as a cloud-service that operates over cloud infrastructure without revealing the individual’s visual information to the cloud service provider (CSP). The proposed P2SLR system is realized through two innovations: (a) block-based probabilistic image encryption scheme that combines fractional-order chaotic system (FOCS) and singular value decomposition (SVD) to obfuscate the visual information in video frames, and (b) a cloud-residing deep convolutional neural network (DCNN) based recognition architecture with a modified classifier to recognize gestures from encrypted video. The proposed scheme is validated for American, Argentinian, and German sign languages. The proposed scheme achieved recognition accuracy in the range 90:76 - 98:09%, comparable to existing state-of-the-art SLR techniques in the plain domain (PD). The proposed image encryption scheme is secure under standard cryptographic image attacks, protecting the individual’s identity. P2SLR is the first move towards developing a secure SLR system over the cloud.</div>

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DuTriNet: Dual-Stream Triplet Siamese Network for Self-Supervised Action Recognition by Modeling Temporal Correlations

Buckchash

¹

,

Raman

²

2020

2020 IEEE 32nd International Conference on Tools With Artificial Intelligence (ICTAI)

5

0

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Dense motion analysis of German finger spellings

Cited by 3 publications

References 39 publications

P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures

P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures

P2SLR: A Privacy-Preserving Sign Language Recognition as-a-Cloud Service Using Deep Learning For Encrypted Gestures

DuTriNet: Dual-Stream Triplet Siamese Network for Self-Supervised Action Recognition by Modeling Temporal Correlations

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