This study presents an approach for detecting examination cheating using Principal Component Analysis (PCA) based face recognition with Eigenfaces. Specifically, we leverage the strengths of PCA and Eigenfaces to automatically identify individuals during an examination, enabling the detection of unverified students. The proposed methodology includes extracting facial features using PCA, which transforms the high-dimensional face data into a lower-dimensional subspace of eigenfaces. The eigenfaces will be considered as a basis for representing facial variations among students. During an examination, real-time face recognition is performed by comparing the captured facial features with those in the existing database. The system detects potential cases of cheating based on discrepancies between the recognized face and the verified students. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying students in various scenarios, even changes in lighting conditions, facial expressions, and minor occlusions.