Integrating State-of-the-Art Face Recognition and Anti-Spoofing Techniques into Enterprise Information Systems

Mishra, Satyam; Thuy, Nguyen Thi Bich; Truong, Cong-Doan

doi:10.1007/978-3-031-45140-9_7

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…It has applications in various fields, including inventory management in retail and vehicle detection for autonomous vehicles [13], [14]. OpenCV also enables face detection and recognition using techniques like Haar-like features and principal component analysis (PCA) [15], [16]. Overall, OpenCV plays a significant role in computer vision by providing a wide range of tools and algorithms for different tasks [17].…”

Section: Introductionmentioning

confidence: 99%

Immersive Virtual Painting: Pushing Boundaries in Real-Time Computer Vision using OpenCV with C++

Mishra,

Trung,

Ngoc

et al. 2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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This paper presents an innovative approach for immersive virtual painting using real-time computer vision techniques. A meticulously crafted color detection algorithm implemented in C++ andOpenCV achieves up to 97.4% accuracy in identifying specified hues from live video feeds. The detected colors are seamlessly translated into vibrant brush strokes rendered on a digital canvas in real-time. The algorithms exhibit remarkable speed, analyzing each frame within 15ms, enabling ultra-low latency painting interactions. Optimization strategies involving parallel processing and code optimizations provide further performance gains. Comparative analysis reveals 3-4x faster execution using C++ over Python for color detection. The platform delivers an intuitive, natural, and uninterrupted painting experience, as validated through user studies. By automating color detection and digital rendering, this research transforms virtual painting from a passive activity to an immersive form of human-computer co-creativity. The fusion of computer vision, rendering algorithms, and optimization techniques establishes new frontiers in interactive digital art platforms and reshapes human-computer collaboration. Highlights:• This research achieves exceptional accuracy in real-time color detection, with up to 97.4% precision in identifying specified hues across thousands of video frames.• The integrated system enables seamless user interaction for natural virtual painting expressions, eliminating disruptive color selection interruptions.• Comparative analysis reveals significant 3-4x performance gains by implementing the algorithms in C++ instead of Python, underscoring the efficiency benefits of C++ for realtime computer vision applications.• User studies validate the immersive experience delivered by the platform, with users highlighting the responsiveness, precision, and intuitive interaction unmatched by traditional virtual painting tools.• The proposed techniques establish a new paradigm in realtime computer vision, pushing the boundaries of virtual creativity platforms and reshaping human-computer collaboration in the arts.

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Section: Introductionmentioning

confidence: 99%

Immersive Virtual Painting: Pushing Boundaries in Real-Time Computer Vision using OpenCV with C++

Mishra,

Trung,

Ngoc

et al. 2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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“…By addressing the technical demanding situations and barriers related to deep studying-primarily based photo captioning, authors offer treasured insights into the modern-day country of the artwork and open avenues for destiny studies in this rapidly evolving discipline. [6]…”

Section: Introductionmentioning

confidence: 99%

DICKT—Deep Learning-Based Image Captioning using Keras and TensorFlow

Vi,

Mishra,

Ngoc

et al. 2022

Proceedings of the Eighth International Conference on Research in Intelligent Computing in Engineering

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This study evaluates a caption generation model's performance using the BLEU Score metric. The model generates descriptions for images, compared to reference captions with single and dual references. Results show a high BLEU Score, suggesting human-like captions. However, BLEU primarily measures linguistic similarity and n-gram overlap, missing full human-generated caption richness. The findings reveal the model's potential to convey image essence in text, but highlight BLEU Score limitations. TensorFlow and Keras are used for model development, acknowledging their widespread use but also their limitations. The research offers insights into caption generation model capabilities and urges a broader perspective on caption quality beyond quantitative metrics. While higher BLEU Scores are generally preferred, a "good" score varies with dataset and context. The study emphasizes a need for a more comprehensive approach to assess the quality and creativity of machine-generated captions.

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Integrating State-of-the-Art Face Recognition and Anti-Spoofing Techniques into Enterprise Information Systems

Cited by 2 publications

References 31 publications

Immersive Virtual Painting: Pushing Boundaries in Real-Time Computer Vision using OpenCV with C++

Immersive Virtual Painting: Pushing Boundaries in Real-Time Computer Vision using OpenCV with C++

DICKT—Deep Learning-Based Image Captioning using Keras and TensorFlow

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