Comparación del rendimiento de algoritmos para la detección de rostros sobre imágenes naturales
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Resumen
La detección de rostros es una de las áreas de investigación más estudiadas en Visión por computadora. Los algoritmos utilizados para la detección de rostros juegan un papel importante en el reconocimiento facial, reconocimiento de expresiones, análisis de sentimientos, entre otros. En este estudio se realiza una comparación del rendimiento de 2 de los algoritmos más utilizados en la literatura para la detección de rostros: Haar Cascade y HOG, ambos algoritmos fueron probados en tres conjuntos de datos derivados del conjunto de datos público Flickr8k (con un rostro, con múltiples rostros y sin rostros). El objetivo fue medir cuál de los 2 otorga el mejor rendimiento en términos de precisión al momento de detectar rostros en escenas. Los resultados muestran que HOG obtuvo un F1-Score superior en ambos experimentos con rostros presentes (0.95 frente a 0.84 en imágenes con un rostro; 0.87 frente a 0.85 en imágenes con múltiples rostros) y un menor número de falsos positivos en los 3 conjuntos evaluados. Sin embargo, Haar Cascades logró detectar un mayor número total de rostros en escenas con múltiples caras, a costa de una mayor tasa de falsos positivos. Ambos algoritmos presentaron tiempos de ejecución equivalentes, por lo que la elección entre uno y otro dependerá de la sensibilidad y precisión deseada según el contexto de aplicación.
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