Simulating the Activity of Archaeological Excavation in the Immersive Virtual Reality

Hung, Yi‐Ping; Huang, Tsuimei; Li, Kuang‐Ti; Rajapakse, R.P.C. Janaka; Chen, Yang-Sheng; Han, Ping-Hsuan; Liu, Iou-Shiuan; Wang, Haocheng; Yi, Da-Chung

doi:10.1109/digitalheritage.2018.8810075

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…This means that the proposed model achieved a high success rate in the field of reconstruction of archaeological things. Yi-Ping Hung et al (2018) proposed a simulating- based archaeological excavation in the virtual platform and achieved better results as compared to other state of the art modules [ 61 ]. Marco Galli et al (2018) described the importance of archaeological contexts to reconstruct the original features of archaeological excavation [ 62 ].…”

Section: Literature Workmentioning

confidence: 99%

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Kumar

Rani²,

Jain

et al. 2022

Sensors

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Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.

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Section: Literature Workmentioning

confidence: 99%

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Kumar

Rani²,

Jain

et al. 2022

Sensors

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“…The first category of articles, for exclusion from this study, consisted of indirect VR dog model use; the dog model was not part of the main purpose of the study. Examples include, haptic forces used for rehabilitation through the use of simulated dog walking (Sorrento et al, 2018), used to facilitate the study (e.g., leads or assists the users to an area as part of a non-dog related study/task (e.g., Hung et al, 2018)) or study conditions (e.g., a red robot dog that barked to distract the user (Rewkowski et al, 2019)). Articles were excluded if they were in 2D due to the reported disadvantages when compared to 3D VR including reduced levels of presence, immersion, and spatial navigation success rates (Slobounov et al, 2015;Minns et al, 2018).…”

Section: Behavioural Dog Modelsmentioning

confidence: 99%

A Systematic Scoping Review of Human-Dog Interactions in Virtual and Augmented Reality: The Use of Virtual Dog Models and Immersive Equipment

Oxley

Santa

Meyer

et al. 2022

Front. Virtual Real.

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Virtual reality is beneficial from a research and education perspective as it allows the assessment of participants in situations that would otherwise be ethically and practically difficult or impossible to study in the real world. This is especially the case where the assessment of human behaviour in the presence of stimuli (e.g. an aggressive dog) is being measured which could potentially constitute a risk in a real-world environment (e.g. a dog bite). Given that the dog is the most popular companion animal species, to date there is limited research that identifies and reviews the use of virtual and augmented reality directly relating to human-dog interactions. Furthermore, there also appears to be no review of the equipment and dog model specifications, such as dog breed and behaviours, which are currently used in these studies. As a result, this systematic scoping review searched ten databases to assess the current use and specifications of dog models which directly focused on human-dog interactions. Ten articles were identified. Six related to assessment or treatment of dog fear/phobia (cynophobia), three included multiple animal phobias, including dogs, and one article investigated the human and virtual dog interactions whilst walking. Six articles used a single breed (German Shepherd, Beagle, Doberman, and Rottweiler). Both the breed and behaviours displayed lacked justification and were often not evidence based. Specific measurements of model quality (e.g., polygons/vertices) were reported in only two articles which may affect repeatability and make comparisons between studies difficult. The virtual reality equipment (e.g. CAVE, head mounted display) and navigation methods (e.g. joystick, mouse, room scale walking) used varied between studies. In conclusion, there is a need for the accurate development and representation, including appearance and behaviours, of dog models in virtual and augmented reality. This is of high importance especially as most of the research covered in this review was conducted with the aim to treat the fear or phobia of dogs.

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Simulating the Activity of Archaeological Excavation in the Immersive Virtual Reality

Cited by 2 publications

References 12 publications

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

A Systematic Scoping Review of Human-Dog Interactions in Virtual and Augmented Reality: The Use of Virtual Dog Models and Immersive Equipment

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