The multimodal biometric recognition technology of smart door lock combines the advantages of multiple biometric recognition methods through algorithm fusion to improve recognition accuracy in different light environments. This fusion is not a simple function superposition, but allows each recognition method to complement each other's shortcomings under the coordination of the algorithm and adapt to the challenges brought by light changes.
In a strong light environment, a single facial recognition may cause facial details to be overexposed and lost due to excessive light, while fingerprint recognition in multimodal technology is not affected by light. The algorithm will prioritize the extraction of fingerprint features for verification, while weakening the overexposed facial information. The algorithm automatically adjusts the weights of different recognition methods by judging the light intensity, allowing the recognition method that is not disturbed by strong light to dominate the verification process, ensuring that the accuracy is not affected by strong light.
In the face of weak light or dark environments, infrared recognition becomes an important supplement. At this time, the visible light imaging effect of facial recognition decreases, and the algorithm will activate the infrared camera to capture the infrared features of the face, and combine fingerprints or other biometrics for cross-verification. Infrared features are not affected by ambient light brightness. The algorithm builds a complete biometric model by fusing infrared images with other feature data to avoid recognition failures caused by insufficient light.
The core of algorithm fusion lies in the intelligent screening and complementarity of feature data. Under different lighting conditions, each biometric recognition method will generate feature data of different quality. The algorithm will evaluate this data and select the feature points with high reliability for fusion. For example, in a backlit environment, the facial contour features are clear but the details are blurred. The algorithm will extract the contour features and combine them with the fingerprint details to form a complete recognition basis and improve the overall accuracy.
The algorithm adjustment mechanism that dynamically adapts to light changes makes the recognition process more flexible. When the light changes drastically in a short period of time, such as a sudden change from indoor to outdoor light, the algorithm can quickly switch the collaborative mode of different recognition methods, continuously collect valid biometric data during the change process, and avoid recognition interruption caused by unstable light. This dynamic adjustment ensures that the recognition accuracy will not fluctuate significantly during the light transition stage.
In addition, the anti-interference model in the algorithm can effectively filter the noise caused by light changes. Light changes may cause interference information such as light spots and shadows to appear in the biometric image. The fusion algorithm uses the trained anti-interference model to identify and eliminate these noises and retain real and effective feature data. At the same time, the model will learn the interference features under different light conditions, continuously optimize the filtering mechanism, and improve the adaptability to complex light environments.
The algorithm fusion of multimodal biometrics also improves recognition reliability through confidence weighting. Each recognition method will generate a confidence score after verification. The algorithm performs weighted calculations on each score according to the light environment to obtain a comprehensive confidence. For example, the confidence of facial recognition is high under medium light, and the weight is increased accordingly; under extreme light, its confidence decreases, the weight decreases, and the weight of other recognition methods increases, and the accuracy of the final recognition result is ensured through comprehensive scoring.
Through this multi-mode collaboration, dynamic weight adjustment, noise filtering and confidence fusion, the multimodal biometric recognition technology of smart door lock can stably extract effective biometric features in various light environments, achieve accurate recognition, and provide reliable protection for the use of smart door lock under different light conditions.
