Analysis of Face Detection Techniques Using Artificial Intelligence

One of the most interesting problems in machine learning is face recognition. Face recognition is an issue that has been solved using a variety of strategies and approaches. In this research, we have demonstrated the effective usage of Principal Component Analysis in conjunction with the K Nearest Neighbors algorithm for face recognition. The K nearest neighbor algorithm is a non-parametric learning technique that determines the query point's final value by utilizing the target values of the K nearest data points. Eigen vectors are a notion used in PCA. An image is represented by an Eigen vector. K higher Eigen values are found by PCA to match to K Eigen vectors. Therefore, the PCA algorithm is a productive way to extract features for facial identification. Python is used as the programming language for implementation. This work illustrates the impact of combining the aforementioned technologies with their cutting-edge outcomes.