Recognition of static gestures using correlation and cross-correlation

Saqib, Shazia; Kazmi, Syed Asad Raza

doi:10.21833/ijaas.2018.06.002

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…*In the last 40 years, automatic computer-based biometric identification has emerged as a strong technique for recognizing an individual's identity. The biometric characteristics derived from human biological organs; such as iris, retina, face, and various hand patterns; including fingerprint, finger knuckle pattern, hand geometry, Palm Print, etc., are grouped as physiological characteristics, while others are called behavioral characteristics; such as gait, voice, eye blinking (Saied et al, 2020), lip movement (Ezz et al, 2020) signature, and gesture (Wang and Geng, 2009;Kumar and Srinivasan, 2012;Abdelwhab and Viriri, 2018;Saqib and Kazmi, 2018). Token and/or password-based methods have historically been used for personal authentication.…”

Section: Introductionmentioning

confidence: 99%

Palm print recognition system using siamese network and transfer learning

al.¹

2022

Int. j. adv. appl. sci.

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This paper proposes a palmprint authentication approach using a one-shot learning technique based on similarity instead of classification (used by most other proposals). The one-shot learning technique uses the siamese network architecture built on top of the pre-trained VGG16 to efficiently reduce the cost and time of training the siamese network. This technique allows the user registration using only one palmprint and then performs the authentication process by performing a siamese similarity measure instead of classification techniques. The proposed model achieved high accuracies scores of 97%, 96.7% for Tongji datasets, 92.3%, 91.9% for PolyU-IITD datasets, 90.9%, 88.3% for CASIA datasets and 95.5% for COEP dataset. These performances were measured based on the testing dataset for unseen persons while the siamese training dataset was applied to different persons. The proposed model uses the pre-trained part of VGG16 as a feature extraction part then feeds the generated feature vector into the Euclidean distance layer that is trained in conjunction with the sigmoid layer to output the final similarity decision. Compared to other models, this proposed model achieved a high average accuracy of 93.2% and 0.19 EER over the available four palm print datasets which is generalized over proposals. All codes are open-source and available online at https://github.com/ProjectsRebository/PalmPrint-recognition-using-Transfer-Learning.

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Section: Introductionmentioning

confidence: 99%

Palm print recognition system using siamese network and transfer learning

al.¹

2022

Int. j. adv. appl. sci.

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“…The information conveyed through gestures is either in the form of static gestures or in the form of continuous gestures [ 1 ]. The continuous gestures are represented by videos [ 2 ]. A video itself cannot be recognized.…”

Section: Introductionmentioning

confidence: 99%

Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method

Saqib

Kazmi

2018

Entropy

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Multimedia information requires large repositories of audio-video data. Retrieval and delivery of video content is a very time-consuming process and is a great challenge for researchers. An efficient approach for faster browsing of large video collections and more efficient content indexing and access is video summarization. Compression of data through extraction of keyframes is a solution to these challenges. A keyframe is a representative frame of the salient features of the video. The output frames must represent the original video in temporal order. The proposed research presents a method of keyframe extraction using the mean of consecutive k frames of video data. A sliding window of size k / 2 is employed to select the frame that matches the median entropy value of the sliding window. This is called the Median of Entropy of Mean Frames (MME) method. MME is mean-based keyframes selection using the median of the entropy of the sliding window. The method was tested for more than 500 videos of sign language gestures and showed satisfactory results.

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“…);Nihal et al (2021);Kenshimov et al (2021) in Japanese Sign Language, Bangla Sign Language and Kazakh Sign Language recognition study respectively Saqib & Kazmi (2018)Ali et al (2020);Ismail et al (2021) in Arabic Sign Language recognition study andPariwat & Seresangtakul (2019) in Thai Sign Language recognition study.…”

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confidence: 98%

A Systematic Literature Review on Vision-Based Hand Gesture for Sign Language Translation

Johari¹,

Ramli²,

Zulkoffli³

et al. 2023

jkukm

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Deaf and hard of hearing people use sign language to communicate. People around mute and deaf people have difficulty communicating with each other if they do not understand sign language. This problem has prompted many researchers to conduct studies on sign language translation. However, there is a lack of compilation of SLR on this topic. Therefore, this paper aims to provide a thorough literature review of previous studies on sign language to text translation based on the vision method. PRISMA (Preferred Reporting Items to writing a standard Systematic Review and Meta-Analyses) is used in this systematic review. Two primary databases, Web of Science and Scopus, have been used to search for relevant articles and resources included in this systematic literature review. Based on the outcome of the systematic review of the topic, the primary studies on sign language translation systems were conducted using self-generated datasets more than public datasets. More static action sign language was studied compared to dynamic action sign language. For the type of recognition, more alphabet sign language was studied compared to digit, word, or sentence sign language. Other than that, most studies used digital cameras rather than Microsoft Kinect or a webcam. The most used classification method was Convolution Neural Network (CNN). The study is intended to guide readers and researchers for future research and knowledge enhancement in the field of sign language recognition.

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Recognition of static gestures using correlation and cross-correlation

Cited by 6 publications

References 20 publications

Palm print recognition system using siamese network and transfer learning

Palm print recognition system using siamese network and transfer learning

Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method

A Systematic Literature Review on Vision-Based Hand Gesture for Sign Language Translation

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