Comparación del rendimiento de algoritmos para la detección de rostros sobre imágenes naturales
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Abstract
Face detection is one of the most studied areas of research in Computer vision. The algorithms used for face detection play a crucial role in facial recognition, expression recognition, sentiment analysis, and other applications. This study compares the performance of two of the most widely used face detection algorithms in the literature: Haar Cascade and HOG. Both algorithms were tested on three datasets derived from the public Flickr8k dataset (one face, multiple faces and no faces at all). The objective was to determine which of the two offers the best performance in terms of accuracy when detecting faces in scenes. The results show that HOG achieved a higher F1-score in both experiments with faces present (0.95 vs. 0.84 in images with one face; 0.87 vs. 0.85 in images with multiple faces) and a lower number of false positives across all 3 evaluated sets. However, Haar Cascades detected a greater total number of faces in scenes with multiple faces, at the cost of a higher false positive rate. Both algorithms had equivalent run times, so the choice between them will depend on the desired trade-off between sensitivity and precision, subject to the application context.
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