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Spatiotemporal CNN with Pyramid Bottleneck Blocks: Application to eye blinking detection.

S E Bekhouche¹, I Kajo², Y Ruichek²

¹CIAD, University Bourgogne Franche-Comté, UTBM, F-90010 Belfort, France; University of the Basque Country UPV/EHU, San Sebastian, Spain.

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|May 7, 2022

Summary

This summary is machine-generated.

This study introduces a fast framework for detecting multiple eye blinks in images, even with varying conditions. The new method improves upon existing techniques for real-world facial analysis tasks.

Keywords:

Eye blinking Facial landmarks Incremental SVD Pyramid Bottleneck Blocks Spatiotemporal CNN

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Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Published on: September 1, 2018

Area of Science:

Computer Vision
Biometrics

Background:

Eye blink detection is crucial for facial analysis tasks like anti-spoofing and driver drowsiness detection.
Existing methods often fail in uncontrolled environments ('in the wild') and struggle with multiple blinks in a sequence.

Purpose of the Study:

To develop a fast and robust framework for detecting and verifying multiple eye blinks from image sequences.
To address challenges like varying lighting, poses, and appearance changes in real-world scenarios.

Main Methods:

Utilizes a fast facial landmark detector to identify key facial points, including eye regions.
Employs a Singular Value Decomposition (SVD)-based method within a sliding window for initial blink detection.
Verifies detected blink candidates using a 2D Pyramidal Bottleneck Block Network (PBBN).
An alternative approach uses a continuous 3D PBBN for frame sequences.

Main Results:

The proposed framework effectively extracts multiple blinks from image sequences under challenging conditions.
Experimental results demonstrate superior performance compared to existing state-of-the-art approaches.

Conclusions:

The developed framework offers a significant advancement in eye blink detection, particularly for real-world applications.
The approach shows promise for enhancing the accuracy and reliability of facial analysis systems.