This work is devoted to fast image recognition techniques based on statistical sequential analysis. We examine the possibility to sequentially process principal components and organize a convolutional neural network with early exits. We pay special attention to sequentially learning a multi-task lightweight neural network model in order to predict several facial attributes (age, gender, and ethnicity) based on preliminary training on the face classification task. We specifically note that the entire above-mentioned model should be fine-tuned in order to deal with the emotion recognition problem. Experimental evaluation on several datasets demonstrates that the proposed approach has high accuracy and very low running time and space complexity compared to known state-of-the-art methods.
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Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 499, 2021, pp. 267–283.
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Savchenko, A.V. Fast Image Classification Algorithms Based on Sequential Analysis. J Math Sci 273, 628–638 (2023). https://doi.org/10.1007/s10958-023-06524-9
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DOI: https://doi.org/10.1007/s10958-023-06524-9