Weighted histogram equalized PEM-PCA face recognition

With high performance characteristic in recognition precision, Principal Component Analysis is widely used in face recognition systems; however, the computational time may be huge, especially during Eigen Vector Decomposition of high matrix and vector manipulation operations in addition to a precision effect on multi-expression of human faces. Thus, this research investigates the possibility to utilize the parallelism of optimized expectation maximization on EVD to mitigate the random affect and then improve the recognition speed-up. We also proposed the recognition optimization utilizing histogram equalization and weighted Euclidean Distance classification derived from Eigenvalues to enhance the recognition precision. These mechanisms, the so-called Weighted Histogram Equalized PEM-PCA or whe-PEM-PCA, result in higher accuracy and lower complexity compared to a traditional PCA and its derivatives leading to a high speed face recognition system.