Abstract
People recognition in digital images has wide applications and challenges. In this article, we present a systematic review of works published in the last decade; based on which, we have identified, implemented and tested the frequently used and best-assessed algorithms. We have found Histograms of Oriented Gradients (HOG) like feature extraction algorithm; and two classification algorithms, AdaBoost and Support Vector Machine (SVM). The tests were performed on 50 images chosen randomly from Penn-Fudan public database. The accuracy in SVM-HOG combination was 0.96, it is a similar value to a related work; and the detection rate was 0.66 in SVM-HOG combination and 0.72 in Adaboost-HOG combination, they are inferior to related works. We shall discuss possible reasons.
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Acknowledgments
The authors are grateful for the partial financial support provided by the Escuela Politécnica Nacional for the development of the project PII-DICC-007-2015.
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Intriago-Pazmiño, M., Vargas-Sandoval, V., Moreno-Díaz, J., Salazar-Jácome, E., Salazar-Grandes, M. (2017). Algorithms for People Recognition in Digital Images: A Systematic Review and Testing. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-56538-5_44
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DOI: https://doi.org/10.1007/978-3-319-56538-5_44
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