Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection

Bera, Asish; Bhattacharjee, Debotosh; Shum, Hubert P. H.

doi:10.1016/j.eswa.2021.114583

Cited by 9 publications

(8 citation statements)

References 48 publications

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“…HandCAPTCHA is implemented using a randomized combination of two real and five to seven fake hand-images [6]. In addition to hand biometric verification, liveness detection is added to the verification pipeline to improve security [7]. Vessel CAPTCHA [16] targets 3D brain vessel segmentation.…”

Section: Generic Image-based Captchasmentioning

confidence: 99%

“…Over the decades, several variations of CAPTCHAs (e.g., textual, image, audio, cognitive, adversarial, visual reasoning, etc.) have been developed to thwart different categories of bot attacks [1,7,15,30,37,57,63,68,70,75]. State-of-the-art methods have emphasized the security and robustness of underlying algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, few methods have applied real-time user verification, or biometric authentication [5], [4] etc. independently, or in conjunction with liveness detection [7,65], spoofing detection [7], [8], to improve security.…”

Section: Introductionmentioning

confidence: 99%

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Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacks

et al. 2023

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Section: Generic Image-based Captchasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacks

et al. 2023

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“…Before the deep learning era, different local shape and texture information including the LBP, geometric properties, shape profiles, bag of words, Scale Invariant Feature Transform (SIFT), colors, and many more have been described in the literature [32], [33], [13], [34], [26], [14], [35], [36]. Most of these conventional feature descriptors are used for recognizing the human faces, emotions, hand-shape, palmprint, skin lesions, and other biometric modality and object categories.…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Networks Fused with Textures for Image Classification

Bera

Bhattacharjee

Nasipuri

2023

Lecture Notes in Networks and Systems

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Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations. Deep learning methods have achieved remarkable success in solving FGIC. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modeling using the long shortterm memory (LSTM). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for image classification. The method is tested on eight datasets representing the human faces, skin lesions, food dishes, marine lives, etc. using four standard backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins.

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“…Human identification using biometric techniques has become the most important issue in recent years [1]. Human identification techniques based on fingerprint and face detection are available.…”

Section: Introductionmentioning

confidence: 99%

Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

Khan¹,

Khan²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

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Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully automated deep learning and improved ant colony optimization (IACO) framework for HGR using video sequences in this work. The proposed framework consists of four primary steps. In the first step, the database is normalized in a video frame. In the second step, two pre-trained models named ResNet101 and InceptionV3 are selected and modified according to the dataset's nature. After that, we trained both modified models using transfer learning and extracted the features. The IACO algorithm is used to improve the extracted features. IACO is used to select the best features, which are then passed to the Cubic SVM for final classification. The cubic SVM employs a multiclass method. The experiment was carried out on three angles (0, 18, and 180) of the CASIA B dataset, and the accuracy was 95.2, 93.9, and 98.2 percent, respectively. A comparison with existing techniques is also performed, and the proposed method outperforms in terms of accuracy and computational time.

show abstract

Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection

Cited by 9 publications

References 48 publications

Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacks

Style matching CAPTCHA: match neural transferred styles to thwart intelligent attacks

Deep Neural Networks Fused with Textures for Image Classification

Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

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