Adaptive Multimodal Identification and Detection

With the rapid development of artificial intelligence and deep learning technologies, multimodal recognition detection has become an important direction in both academic research and practical applications. This paper proposes an adaptive mechanism-based multimodal recognition detection method that integrates five core functions: gesture recognition, action recognition, speech recognition, iris localization detection, and simulated obstacle drag recognition. By improving convolutional neural network (CNN) and local binary pattern (LBP) algorithms, this paper achieves efficient feature extraction and visual identification. Experimental results show that the proposed method performs excellently in improving recognition accuracy and speed, especially in complex environments and multimodal interaction tasks.