<![CDATA[The field of statistical pattern recognition is concerned with utilizing statistical techniques to find and categorize patterns in data. This field of study has connections to signal interpreting, computer vision, and machine learning. The initial work on statistical pattern recognition was introduced in the 1960s. Later in the 1990s, it grabbed attention in the fields of Bioinformatics, Geography, Wildlife, and Character recognition by using eigenfaces, fisherfaces, KNN, Fuzzy classification, and Cluster analysis. In this paper, we have used two supervised techniques, Eigenfaces and Fisherface, for face recognition. R (version 4.4.1) software was used for this study. Eight hundred face images of the inhabitants of Pakistan (200 face images from each of the 4 provinces) were used. Using the eigenfaces technique, it was found that 20% of images from Balochistan, 10% of images from Khyber Pakhtunkhwa, and 10% of images from Sindh were not correctly identified. In comparison, 100% of images from Punjab were correctly identified, resulting in an overall 10% error rate. By using the Fisher faces technique, it was found that 3% of images from Balochistan, 2% of images from Khyber Pakhtunkhwa, 0.5% of images from Punjab, and 2% of face images from Sindh were not correctly identified, resulting in overall 1.75% error rate. This study illustrates Fisherfaces' higher efficacy for facial recognition accuracy across multiple geographic representations.]]>
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