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Generating Fuzzy Rules for Wildfire Pixel Segmentation Using Genetic Programming and Color Content
(Springer Nature Switzerland, 2026)
Lopez-Alanis, Alberto
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De-la-Torre-Gutierrez, Hector
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Hernández-Aguirre, Arturo
;
Álvarez-Pato, Víctor M.
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Image Facial Expression Recognition based on Active Muscles and their Notable Triangle Points
(IEEE, 2025)
Aguilera-Hernández, Edgar I.
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Cruz-Aceves, Ivan
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Hernández-Aguirre, Arturo
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Moya-Albor, Ernesto
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Brieva, Jorge
During emotion experience originated in psychological changes, the effect in face muscles results in a characteristic set of contractions associated to specific emotions. This paper propose an intuitive representation of these interactions with the objective of facial expression recognition through geometric features. In medical research, it has generated insights regarding emotional state, cognitive function, and pain level during clinical procedures leading to an effective patient treatment, assisting diagnosis and monitoring disease progression mainly in neurological conditions. Starting from a facial muscle modeling using triangles, it utilizes an initial 68 landmarks fitting algorithm, and later the computation of triangle notable points to work as anchors of specific muscles. Secondly, the optimization process through stochastic techniques is applied to set the point type combination so that the F1-Score is maximized. Experimental results were performed with conventional classifiers and no fine tuning, accomplishing an accuracy, precision, recall and F1-score of 0.88 for KDEF dataset, while 0.84, 0.86, 0.84, and 0.84 respectively for the JAFFE dataset, proving to be a reliable technique in the expression recognition problem. ©The authors ©IEEE.
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Fuzzy Rule-Based Combination Model for the Fire Pixel Segmentation
(Institute of Electrical and Electronics Engineers (IEEE), 2025)
Lopez-Alanis, Alberto
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de-la-Torre-Gutierrez, Hector
;
Hernández-Aguirre, Arturo
;
Orvañanos-Guerrero, María T.
Color-feature-based wildfire pixel segmentation has become a challenging task extensively addressed in various research studies. Rule-based models aim to identify fire pixels in a binary manner by determining whether the pixel intensity exceeds a specified threshold value. The authors determine the thresholds by analyzing diverse collections of images that contain wildfires. This has resulted in a lack of consensus on the thresholds determined by various researchers, even when the same color space is used during the examination process. Additionally, determining fire pixels in a binary manner complicates the handling of uncertainty and vagueness in color information. This research aims to enhance fire-pixel segmentation by integrating color-based rule models with a fuzzy set approach, which effectively addresses uncertainty and vagueness. The proposed approach automatically learns the optimal set of fuzzy operators and rules for fire detection to construct a combined model. To address the limitations of combining binary class labels, this approach modifies the rule form proposed by various authors to obtain a fuzzy set of data, such as a grayscale fire map, instead of a crisp set of data, such as a binary fire map. In addition, our proposal uses a genetic algorithm approach to construct the best combination model. The final binary form of the fire map is calculated using the widely used Otsu method. The presented method is evaluated qualitatively and quantitatively in a well-accepted dataset designed for wildfire pixel segmentation tasks. The model obtained outperforms state-of-the-art rules and traditional strategies for combining binary labels in the F-measure and IoU metrics. © 2013 IEEE.

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