Face Image Recognition by using some Dimension Reduction Algorithms and Logistic Regression

Abstract

Face recognition is a common technique for artificial intelligence and image processing in which machine-learning algorithms automatically recognize a person's face through a huge image collection or even from a live video since there is difficulty in detecting data when using different shots for the same person in a facial recognition system, it takes time to determine person's membership. Furthermore, when working with high-dimensional data, where the 2D-pixel values of facial images, can be computationally challenging, the face recognition problem gets harder, so the current research compares the dimensionality reduction techniques like the principal component analysis algorithm PCA, an unsupervised learning algorithm that reduces dimensions by moving them from a high-dimensional area to a lower area without losing important information, and the two-dimensional principal component analysis algorithm 2DPCA to determine which technique is most effective for the process of detecting facial images, and this method will be compared with the logistic regression which is a statistical model for face recognition that has been used for classification tasks. Two different sets of data were used, the first group represented the ORL database, which was made up of 40 individuals, while the second group represented the real data, which was made up of 100 individuals from AL-Mamoon College, the results showed that the logistic regression model achieved the lowest MSE for ORL dataset and real data by achieving (0.0024414, 0.091936) respectively, followed by 2DPCA and PCA and all three techniques reached the highest accuracy rate of 100% for facial image recognition on real data. Paper type: Research paper