Electronic Security System Integration

York, Tony W.; MacAlister, Don

doi:10.1016/b978-0-12-420048-7.00019-2

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Researchers have put a lot of effort into tackling the problem by looking into several clues. Much research is ongoing on improving hardware-based recognition systems, such as the use of thermal cameras [4] and 3D cameras [5]. However, the high cost of these solutions is still a challenge despite the improvement in identifying fake and real faces.…”

Section: Introductionmentioning

confidence: 99%

Influence of Standalone and Ensemble Classifiers in Face Spoofing Detection using LBP and CNN Models

Prasad,

Jain,

Bhanodia

et al. 2024

EJECE

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Background: Artificial intelligence has made significant contributions to facial recognition and biometric identification and is now being employed in a range of applications. Detecting facial spoofing, where someone attempts to pass as an authorized user to gain access to the system, is still difficult. Spoofing-attack-resistant face recognition systems demand efficient and effective solutions. A more stringent recognition system will result in higher false positives and false negatives, which makes such a system questionable for practical use. Eventually, the prominent deep-learning techniques were overtaken by CNN-based architecture. Objective: To analyse classifiers to identify the impact on spoof detection. The intent is not only to get the highest accuracy but also to find strategies to significantly reduce false positives and false negatives. Methods: Face image spoofing detection is implemented in this paper by extracting face embedding using the Local Binary Pattern (LBP) and the VGG16 CNN architecture. To classify real and spoof images, SVM, KNN, Decision Tree, and ensembles of classifier models are utilized. Results: The proposed three models obtained test accuracy of 98%, 94.48%, and 99% when applied to the custom dataset, while in the NUAA photography imposter dataset, they achieved 97%, 99%, and 100% and kept the FN and FP significantly low. Conclusion: Accessing human faces through smart gadgets from various resources is possible, leading to the possibility of spoof attacks. Although spoof detection methods persist, effective methods with high accuracy and low FN and FP are still required. The proposed ensemble techniques significantly outperform the existing classifiers with high accuracy, keeping FN and FP low.

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Section: Introductionmentioning

confidence: 99%

Influence of Standalone and Ensemble Classifiers in Face Spoofing Detection using LBP and CNN Models

Prasad,

Jain,

Bhanodia

et al. 2024

EJECE

View full text Add to dashboard Cite

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Dog whistles, covertly coded speech, and the practices that enable them

Quaranto

2022

Synthese

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Dog whistling-speech that seems ordinary but sends a hidden, often derogatory message to a subset of the audience-is troubling not just for our political ideals, but also for our theories of communication. On the one hand, it seems possible to dog whistle unintentionally, merely by uttering certain expressions. On the other hand, the intention is typically assumed or even inferred from the act, and perhaps for good reason, for dog whistles seem misleading by design, not just by chance. In this paper, I argue that, to understand when and why it's possible to dogwhistle unintentionally (and indeed, intentionally), we'll need to recognize the structure of our linguistic practices. For dog whistles and for covertly coded speech more generally, this structure is a pair of practices, one shared by all competent speakers and the other known only to some, but deployable in the same contexts. In trying to identify these enabling conditions, we'll discover what existing theories of communicated content overlook by focusing on particular utterances in isolation, or on individual speakers' mental states. The remedy, I argue, lies in attending to the ways in which what is said is shaped by the temporally extended, sociopolitically structured linguistic practices that utterances instantiate.

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Electronic Security System Integration

Cited by 2 publications

References 3 publications

Influence of Standalone and Ensemble Classifiers in Face Spoofing Detection using LBP and CNN Models

Influence of Standalone and Ensemble Classifiers in Face Spoofing Detection using LBP and CNN Models

Dog whistles, covertly coded speech, and the practices that enable them

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