Research On the Development of Intelligent Face Recognition System for Business Competition in Chatgpt Industry
DOI:
https://doi.org/10.54097/8kfjg139Keywords:
Human Behavior Recognition; Face Recognition; Feature Extraction; ChatGPT; Graph Convolutional Neural Network; Laplace's Algorithm.Abstract
Artificial intelligence has become a key area of today's international competition. Analysis of the development trend of artificial intelligence and the strategies of major powers is crucial to winning this competition. This paper refers to (GCN) and Laplace's (LBP) feature equation in ChatGPT. Firstly, an image processing method based on LBP is proposed. GCN analyzes the temporal and spatial correlation of character attributes in the time domain. This method can capture both short-term and long-term temporal and situational correlations simultaneously, effectively improving the extraction of temporal and situational correlations. Then, text analysis determines the implementation intentions, measures and prospects of the EU's "human-centered" artificial intelligence competition strategy. It also evaluates its strategic effects from the relevant reactions of its main competitor, the United States, and infers the weaknesses and risks existing in the strategy.
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