Parallel Optimized Pearson Correlation Condition (PO-PCC) for Robust Cosmetic Makeup Facial Recognition

Makeup changes or the application of cosmetics constitute one of the challenges for the improvement of the recognition precision of human faces because makeup has a direct impact on facial features, such as shape, tone, and texture. Thus, this research investigates the possibility of integrating a statistical model using Pearson Correlation (PC) to enhance the facial recognition accuracy. PC is generally used to determine the relationship between the training and testing images while leveraging the key advantage of fast computing. Considering the relationship of factors other than the features, i.e., changes in shape, size, color, or appearance, leads to a robustness of the cosmetic images. To further improve the accuracy and reduce the complexity of the approach, a technique using channel selection and the Optimum Index Factor (OIF), including Histogram Equalization (HE), is also considered. In addition, to enable real-time (online) applications, this research applies parallelism to reduce the computational time in the pre-processing and feature extraction stages, especially for parallel matrix manipulation, without affecting the recognition rate. The performance improvement is confirmed by extensive evaluations using three cosmetic datasets compared to classic facial recognitions, namely, principal component analysis and local binary pattern (by factors of 6.98 and 1.4, respectively), including their parallel enhancements (i.e., by factors of 31,194.02 and 1577.88, respectively) while maintaining high recognition precision