OPTIMIZATION OF FACIAL EXPRESSION MODELS: GRADIENT ENHANCEMENT AND ITS SIGNIFICANCE IN HYPERPARAMETER ADJUSTMENT AND REGULARIZATION
Keywords:
Cross-validation, Grid search, Random search, Early stopping, , L1 regularizationAbstract
In recent years, facial expression recognition has received much attention due to its wide application in various fields, including human-computer interaction, emotion analysis, and facial biometrics. Developing accurate and robust facial expression recognition models is essential to achieve reliable results. This article discusses the importance of optimizing facial expression recognition models and the role of gradient boosting in model optimization. In addition, hyperparameter tuning, regularization methods, data augmentation, transfer learning, and performance metrics are discussed that contribute to the optimization process
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Copyright (c) 2024 Muhammadmullo Asrayev , Abduraxmon Kurbanov , Voxid Fayziyev
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