Combining CNNs and Markov-like Models for Facial Landmark Detection with Spatial Consistency Estimates Article Swipe

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Ahmed Gdoura , Markus Degünther , Birgit Lorenz , Alexander Effland ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.3390/jimaging9050104

The accurate localization of facial landmarks is essential for several tasks, including face recognition, head pose estimation, facial region extraction, and emotion detection. Although the number of required landmarks is task-specific, models are typically trained on all available landmarks in the datasets, limiting efficiency. Furthermore, model performance is strongly influenced by scale-dependent local appearance information around landmarks and the global shape information generated by them. To account for this, we propose a lightweight hybrid model for facial landmark detection designed specifically for pupil region extraction. Our design combines a convolutional neural network (CNN) with a Markov random field (MRF)-like process trained on only 17 carefully selected landmarks. The advantage of our model is the ability to run different image scales on the same convolutional layers, resulting in a significant reduction in model size. In addition, we employ an approximation of the MRF that is run on a subset of landmarks to validate the spatial consistency of the generated shape. This validation process is performed against a learned conditional distribution, expressing the location of one landmark relative to its neighbor. Experimental results on popular facial landmark localization datasets such as 300 w, WFLW, and HELEN demonstrate the accuracy of our proposed model. Furthermore, our model achieves state-of-the-art performance on a well-defined robustness metric. In conclusion, the results demonstrate the ability of our lightweight model to filter out spatially inconsistent predictions, even with significantly fewer training landmarks.

Related Topics

Landmark

Computer Science

Artificial Intelligence

Markov Random Field

Convolutional Neural Network

Computer Vision

Conditional Random Field

Concepts

Landmark Computer science Artificial intelligence Markov random field Pattern recognition (psychology) Robustness (evolution) Convolutional neural network Feature extraction Computer vision Face (sociological concept) Conditional random field Image (mathematics) Image segmentation Gene Sociology Social science Biochemistry Chemistry

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/jimaging9050104
PDF: https://www.mdpi.com/2313-433X/9/5/104/pdf?version=1684800489
OA Status: gold
Cited By: 8
References: 56
Related Works: 10
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DOI: https://doi.org/10.3390/jimaging9050104

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Title: Combining CNNs and Markov-like Models for Facial Landmark Detection with Spatial Consistency Estimates

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Language: en

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Publication year: 2023

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Publication date: 2023-05-22

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Authors: Ahmed Gdoura, Markus Degünther, Birgit Lorenz, Alexander Effland

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Landing page: https://doi.org/10.3390/jimaging9050104

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Concepts: Landmark, Computer science, Artificial intelligence, Markov random field, Pattern recognition (psychology), Robustness (evolution), Convolutional neural network, Feature extraction, Computer vision, Face (sociological concept), Conditional random field, Image (mathematics), Image segmentation, Gene, Sociology, Social science, Biochemistry, Chemistry

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Cited by: 8

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Citations by year (recent): 2025: 3, 2024: 5

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