Abstract
Human face detection is important in many applications, such as human–machine interface, automatic surveillance, and facial recognition. This work exposes a solid and general face detection system capable of detecting multiple faces in the same image, even in low light situations and chaotic backgrounds. The detection system uses a representation of the Gaussian pyramid and evaluates it in all scales the existence of faces using descriptors of HoG characteristics and linear classifiers SVM. The system shows that the gradient distribution in the face contours is sufficiently discriminative to distinguish faces and non-faces and the use of cascade detectors improves overall system performance by decreasing the number of false positives. Employing experimental tests, the methodology was applied to facial and non-facial test images, allowing the evaluation of the effectiveness of the face detection system and the influence of adjustable parameters on the accuracy and performance of the system.
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Duarte, L., Bernadelli, C. (2021). HoG Multi-face Detection. In: Iano, Y., Arthur, R., Saotome, O., Kemper, G., Padilha França, R. (eds) Proceedings of the 5th Brazilian Technology Symposium. BTSym 2019. Smart Innovation, Systems and Technologies, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-030-57548-9_1
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DOI: https://doi.org/10.1007/978-3-030-57548-9_1
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