A person can easily determine the emotional state of the interlocutor standing in front of him with the help of his senses such as seeing, understanding and feeling, but for computer systems this is a very complicated matter. Modern computer technology and artificial intelligence systems provide effective predictions for automatic emotion recognition, but even deep learning models built with huge data sets using modern CNN technologies have proven to be imperfect at identifying complex emotional states. In this article, we will solve the problem of determining the facial features necessary for the correct assessment of the emotional state.
| Mualliflar | Kurbanov, Abduraxmon |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2024-10-20 |
| Son | 3 |
| Betlar | 61-67 |
| Til | Rus |
Local binary pattern, Histogram of Oriented Gradients, konvolutsion neyron tarmoqlari, SVM, k-NN, random forest, Haarcascads, bilateralFilter, Local binary pattern, Histogram of Oriented Gradients, convolutional neural networks. SVM, k-NN, random forest, Haarcascads, bilateralFilter., : Local binary pattern (mahalliy ikkilik naqshlar), Histogram of Oriented Gradients (yo‘naltirilgan gradientlar gistogrammasi), konvolutsion neyron tarmoqlari. SVM, k-NN, random forest, Haarcascads, bilateralFilter.
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