Geodesic
Fast image classification algorithms based on sequential analysis
Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part I, Tome 499 (2021), pp. 267-283 
A. V. Savchenko. Fast image classification algorithms based on sequential analysis. Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part I, Tome 499 (2021), pp. 267-283. http://geodesic.mathdoc.fr/item/ZNSL_2021_499_a14/
@article{ZNSL_2021_499_a14,
     author = {A. V. Savchenko},
     title = {Fast image classification algorithms based on sequential analysis},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {267--283},
     year = {2021},
     volume = {499},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_2021_499_a14/}
}
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Voir la notice du chapitre de livre provenant de la source Math-Net.Ru

In this paper fast image recognition techniques based on statistical sequential analysis are discussed. We examine the possibility to sequentially process the principal components and organize a convolutional neural network with early exits. Particular attention is paid to sequentially learn multi-task lightweight neural network model to predict several facial attributes (age, gender and ethnicity) based on preliminary training on the face classification task. It is highlighted that the whole above-mentioned model should be fine-tuned in order to deal with emotion recognition problem. Experimental study on several datasets demonstrate that the proposed approach is rather accurate and has very low run-time and space complexity when compared to known state-of-the-art methods.

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