In-air hand gesture signature using transfer learning and its forgery attack

Khoh, Wee How; Pang, Ying Han; Teoh, Andrew Beng Jin; Yin, Ooi Shih

doi:10.1016/j.asoc.2021.108033

Cited by 11 publications

(5 citation statements)

References 30 publications

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“…Khoh et al in [25] proposed a novel in-air hand gesture signature verification in order to study the feasibility of transfer learning in classifying a hand gesture-based signature. To release their system, the authors detected and segmented the hand region from each depth image.…”

Section: Related Workmentioning

confidence: 99%

In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Alobaidi,

Dhieb,

Hamdani

et al. 2023

IEEE Access

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Believing that biometrics trends are moving to distant and contactless mode, in-air signature verification is nowadays considered as one of the principal users biometric identification in contactless mode allowing users identification by drawing their handwritten signature in the air. In-air signature verification is used in many applications like access control and forensic analysis. In this regard, we propose a novel system for in-air signature verification using Beta stroke segmentation. The Beta-elliptical approach and the fuzzy perceptual detector are used for features extraction. The proposed system defines a specific data acquisition protocol and uses preprocessing steps to prepare data. Finally, the verification phase is done based on Dynamic Time Warping. To evaluate our proposed system, we have created two in-air signature datasets with and without the use of a transparent glass plate, which we make publicly available at https://ieeedataport.org/documents/air-signature-databases, termed respectively In-Air Signature dataset (IAS dataset) and In-Air Signature dataset using Glass Plate (IASGP dataset). Our verification system demonstrates promising outcomes, yielding an Equal Error Rate (EER) of 1.25% when applied to the IAS dataset and an EER of 2.00% when applied to the IASGP dataset in the skilled-forgery scenario. Extensive evaluations were conducted on both the 3DAirSig and the DeepAirSig datasets. The results confirm that the proposed system has a good performance compared to existing in-air signature verification systems for both skilled-forgery and random-forgery scenarios. INDEX TERMSIn-air signature verification; Beta-elliptical approach; fuzzy perceptual detector; Dynamic Time Warping.

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Section: Related Workmentioning

confidence: 99%

In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Alobaidi,

Dhieb,

Hamdani

et al. 2023

IEEE Access

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“…Like PSO and GTO, EHO also falls under the category of swarm intelligence meta-heuristic algorithms. The position of an elephant is updated using Equation (17).…”

Section: Elephant Herding Optimizationmentioning

confidence: 99%

“…This concept is known as transfer learning and works very well for two similar and unique classification tasks. The two main advantages of transfer learning are a reduction in training time and competence to work well on small datasets [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

Nagarajan,

Chakravarthy,

Venkatesan

et al. 2023

Diagnostics

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One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient’s histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.

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“…In any Deep learning model, feature extraction is an important phase that affects the performance of the algorithm [6], where the database size is considered a significant factor. Transfer learning presents a viable alternative solution when dealing with limited sample size problems that supports taking the knowledge acquired from a previously trained model including features, weights, and other relevant information which was trained on a large dataset such as the ImageNet database, that contains 1.2 million images grouped into 1000 classes to solve the problem of small size dataset in the new target domain [7]. By utilizing a pre-trained model, significant amounts of time spent on training can be saved, and the model can be adapted to work with smaller datasets through retraining [8].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Digital Image Forgery Detection Using Transfer Learning

Khalil,

Ghalwash,

Elsayed

et al. 2023

IEEE Access

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Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it's crucial to identify these forgeries. This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detecting only one type of forgery, such as image splicing or copy-move that is not applied in real life. This paper proposes an approach, to enhance digital image forgery detection using deep learning techniques via transfer learning to uncover two types of image forgery at the same time, The proposed technique relies on discovering the compressed quality of the forged area, which normally differs from the compressed quality of the rest of the image. A deep learning-based model is proposed to detect forgery in digital images, by calculating the difference between the original image and its compressed version, to produce a featured image as an input to the pre-trained model to train the model after removing its classifier and adding a new fine-tuned classifier. A comparison between eight different pre-trained models adapted for binary classification is done. The experimental results show that applying the technique using the adapted eight different pre-trained models outperforms the state-of-the-art methods after comparing it with the resulting evaluation metrics, charts, and graphs. Moreover, the results show that using the technique with the pre-trained model MobileNetV2 has the highest detection accuracy rate (around 95%) with fewer training parameters, leading to faster training time.

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In-air hand gesture signature using transfer learning and its forgery attack

Cited by 11 publications

References 30 publications

In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

Enhancing Digital Image Forgery Detection Using Transfer Learning

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