| dc.description.abstract | Pains in newborn babies are monitored in a Neonatal Intensive Care Unit (NICU) for
medical treatment. Pain in newborns can be detected by studying their facial appearance.
Even though the outcome is acceptable, it is not adequately vigorous to be used in
unpredictable, non-ideal situations such as noise and varying illumination environment.
First, to improve the noise cancellation robustness an adaptive median filter (AMF) is
proposed. Mean and variance of median values are selected to generate a weight for
each window part of the images such as 3x3, 5x5 or 7x7. Various linear and nonlinear
filters are adopted to eliminate the noise in the images. Quantitative comparisons are
performed between these filters with our AMF in terms of Peak Signal-to-Noise Ratio
(PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Mean
Structural SIMilarity (MSSIM) Index. The average results show improvement in terms
of 40.63 db for PSNR, 6.01 for MSE, 258.09 for IEF and 0.97 for MSSIM respectively.
In this work a novel method of illumination invariant normalization known as Modified
Retinex Normalization (MRT) for preprocessing of infant face recognition is proposed.
This is based on a modified retinex model that combines with histogram normalization
for filtering the illumination invariant. The proposed method is compared to other
methods like Single scale Retinex (SSR), Homomorphic method (HOMO), Single Scale
Self Quotient Image (SSQ), Gross and Brajovic Technique (GBT), DCT-Based
Normalization (DCT), Gradientfaces-based normalization technique (GRF), Tan and
Triggs normalization technique (TT), and Large-and small-scale features normalization
technique (LSSF) for evaluation with Infant Classification of Pain Expressions (COPE)
database. Several experiments were performed on COPE databases. Single PCA, LBP
and DCT feature extraction information yielded a good recognition result. However, by
summing these three, it gives more robustness to noise and illumination classification
rate because the sum rule was the most resilient to estimate errors and gives higher than
90% accuracies of pain and no pain detection. The new illumination normalization and
combination of features gives higher results of more than 90% on five different
classifiers with various algorithms such as k-nearest neighbors (k-NN), Fuzzy k-nearest
neighbors (FkNN), Linear Discriminat Analysis (LDA), Feed Forward Neural Network
(FFNN), Probabilistic Neural Network (PNN), General regression Neural Network
(GRNN), SVM Linear kernel (SVMLIN), SVM RBF kernel (SVMRBF), SVM MLP
kernel (SVMMLP) and SVM Polynomial kernel (SVMPOL) with different performance
measurement such as Sensitivity, Specificity, Accuracy, Area under Curve (AUC),
Cohen's kappa (k), Precession , F-Measure and Time Consumption . | en_US |
