Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Parkinson's disease focused multimodal wearable vision based gait monitoring system with real time feedback to support mobility and caregiver well-being.

Discover mental health·2026

Same author

A novel framework for human object classification in flooded environments using YOLOv12n and FastGRNN-MBT.

Scientific reports·2026

Same author

AI-driven audio-to-video generation for dynamic content creation via stable diffusion and CNN-augmented transformers.

Scientific reports·2026

Same author

Adaptive network based fuzzy inference system efficientnet for autism spectrum disorder detection with optimization based pivotal region extraction.

Psychiatry research. Neuroimaging·2025

Same author

Grapes leaf disease dataset for precision agriculture.

Data in brief·2025

Same author

Enhancing semantic segmentation for autonomous vehicle scene understanding in indian context using modified CANet model.

MethodsX·2025

Search research articles

Related Experiment Video

Updated: May 10, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

An enhanced light weight face liveness detection method using deep convolutional neural network.

Swapnil R Shinde^1,2, Anupkumar M Bongale³, Deepak Dharrao¹

¹Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra 412115, India.

|March 19, 2025

Summary

This study introduces LwFLNeT, a lightweight deep CNN, to combat face spoofing attacks. It effectively detects both 2D and 3D attacks using a novel dual-stream architecture with parallel dropout layers.

Keywords:

Biometrics authentication Deep convolution neural network Face spoofing detection Light-weight architecture LwFLNeT

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 10, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Biometric authentication, including face recognition, is crucial for security but vulnerable to sophisticated face spoofing attacks using 2D and 3D methods.
Current anti-spoofing measures often rely on attack-specific designs and complex architectures, leading to high computational costs.
Existing deep transfer learning models, while effective, are computationally expensive for real-world applications.

Purpose of the Study:

To propose LwFLNeT, a novel, lightweight deep Convolutional Neural Network (CNN) architecture for robust face spoofing attack detection.
To design a generalized and efficient method capable of detecting both 2D and 3D face spoofing attempts.
To validate the proposed method's performance against state-of-the-art techniques using cross-dataset evaluations.

Main Methods:

Development of a Light Weight Dual Stream CNN architecture incorporating parallel dropout layers to mitigate overfitting.
Implementation of a generalized deep CNN design to address both 2D and 3D face spoofing modalities.
Extensive validation using cross-dataset train-test evaluation and standard performance metrics.

Main Results:

The proposed LwFLNeT achieves excellent performance in detecting both 2D and 3D face spoofing attacks.
The lightweight dual-stream CNN architecture effectively minimizes overfitting through parallel dropout layers.
The generalized CNN architecture demonstrates higher efficiency compared to existing methodologies in detecting diverse spoofing attacks.

Conclusions:

LwFLNeT offers an efficient and robust solution for face spoofing detection, outperforming current methods.
The novel architecture effectively handles both 2D and 3D spoofing attacks with reduced computational overhead.
This research contributes a computationally inexpensive yet highly effective deep learning model for enhanced biometric security.