Abstract: Embodiments of the disclosure provide a recognition method, apparatus, device, and storage medium and relates to the field of artificial intelligence technology. The method includes obtaining a second decision-making threshold of a feature-matching model in a target scenario by joint testing of the feature-matching model and auxiliary detection model. The method takes into full consideration the mutual influence between different algorithm models in a scenario when multiple algorithm models are used for facial recognition. Compared to manually setting a decision-making threshold for each algorithm model independently, the methods in the disclosure are more adaptable to changing scenarios and scenarios with multiple models used in facial recognition. This improves the accuracy and efficiency of the obtained decision-making thresholds, thereby enhancing the accuracy of multi-model facial recognition.

Abstract: Embodiments of the disclosure provide a recognition method, apparatus, device, and storage medium and relates to the field of artificial intelligence technology. The method includes obtaining a second decision-making threshold of a feature-matching model in a target scenario by joint testing of the feature-matching model and auxiliary detection model. The method takes into full consideration the mutual influence between different algorithm models in a scenario when multiple algorithm models are used for facial recognition. Compared to manually setting a decision-making threshold for each algorithm model independently, the methods in the disclosure are more adaptable to changing scenarios and scenarios with multiple models used in facial recognition. This improves the accuracy and efficiency of the obtained decision-making thresholds, thereby enhancing the accuracy of multi-model facial recognition.