Impact of Shifting Time-Window Post-Processing on the Quality of Face Detection Algorithms - Publikacja - MOST Wiedzy

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Impact of Shifting Time-Window Post-Processing on the Quality of Face Detection Algorithms

Abstrakt

We consider binary classification algorithms, which operate on single frames from video sequences. Such a class of algorithms is named OFA (One Frame Analyzed). Two such algorithms for facial detection are compared in terms of their susceptibility to the FSA (Frame Sequence Analysis) method. It introduces a shifting time-window improvement, which includes the temporal context of frames in a post-processing step that improves the classification quality. Error measures are proposed to express the frame-wise accuracy of classifying algorithms, as well as the segmentation of the result sequences which they produce. The two compared algorithms, after applying the FSA improvement, perform better in terms of all the considered measures. The performed experiments have allowed to draw conclusions regarding preferred methods of measuring accuracy of such algorithms and the selection of suitable classification algorithms for being improved. In the end of the work, the resulting future possibilities of further developing the FSA methods are noted.

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Informacje szczegółowe

Kategoria:
Aktywność konferencyjna
Typ:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Tytuł wydania:
2018 11th International Conference on Human System Interaction (HSI) strony 77 - 83
Język:
angielski
Rok wydania:
2018
Opis bibliograficzny:
Blokus A., Krawczyk H.: Impact of Shifting Time-Window Post-Processing on the Quality of Face Detection Algorithms// 2018 11th International Conference on Human System Interaction (HSI)/ Gdańsk: , 2018, s.77-83
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/hsi.2018.8431293
Źródła finansowania:
Weryfikacja:
Politechnika Gdańska

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