Aboul Ella Hassanien (Abo)

Recently, heart sound signals have been used in the detection of the heart valve status and the identification of the heart valve disease. Due to these characteristics, therefore, two feature reduction techniques have been proposed prior applying data classifications in this paper. The first technique is the chi-Square which measures the lack of independence between each heart sound feature and the target class, while the second technique is the deep believe network that uses to generate a new data set of a reduced number of features according the partition of the heart signals. The importance of feature reduction prior applying data classification is not only to improve the classification accuracy and to enhance the training and testing performance, but also it is important to detect which of the stages of heart sound is important for the detection of sick people among normal set of people, and which period important for the classification of heart murmur. Different classification algorithms including naive bayesian tree classifier and sequential minimal optimization was applied on three different data sets of 100 extracted features of the heart sound. The extensive experimental results on the heat sound signals data set demonstrate that the proposed approach outperforms other classifiers and providing the highest classification accuracy with minimized number of features.

n/a

In this paper the possibility of using the human heart sounds as a human print is investigated. To evaluate the performance and the uniqueness of the proposed approach, tests using a high resolution auscultation digital stethoscope are done for nearly 80 heart sound samples. The verification approach consists of a robust feature extraction with a specified configuration in conjunction with Gaussian mixture modeling. The similarity of two samples is estimated by measuring the difference between their log-likelihood similarities of the features. The experimental results obtained show that the overall accuracy offered by the employed Gaussian mixture modeling reach up to 85%. The identification approach consists of a robust feature extraction with a specified configuration in conjunction with LBG-VQ. The experimental results obtained show that the overall accuracy offered by the employed LBG-VQ reach up to 88.7%

Face extraction is considered a very important step in devel-
oping a recognition system. It is a challenging task as there are dierent
face expressions, rotations, and artifacts including glasses and hats. In
this paper, a face extraction model is proposed for thermal IR human face
images based on superpixel technique. Superpixels can improve the com-
putational eciency of algorithms as it reduces hundreds of thousands of
pixels to at most a few thousand superpixels. Superpixels in this paper
are formulated using the quick-shift method. The Quick-Shift's superpix-
els and automatic thresholding using a simple Otsu's thresholding help
to produce good results of extracting faces from the thermal images. To
evaluate our approach, 18 persons with 22,784 thermal images were used
from the Terravic Facial IR Database. The Experimental results showed
that the proposed model was robust against image illumination, face
rotations, and dierent artifacts in many cases compared to the most
related work.

This paper proposes a human thermal face recognition approach with two variants based on Random linear
Oracle (RLO) ensembles. For the two approaches, the Segmentation-based Fractal Texture Analysis (SFTA) algorithm was used for extracting features and the RLO ensemble classifier was used for recognizing the face from its thermal image. For the dimensionality reduction, one variant (SFTALDA-RLO) was used the technique of Linear Discriminant Analysis (LDA) while the other variant (SFTA-PCA-RLO) was used the Principal Component Analysis (PCA). The classifier’s model was built using the RLO classifier during the training phase and in the testing phase then this model was used to identify the unknown sample images. The two variants were evaluated using the Terravic Facial IR Database and the experimental results showed that the two variants achieved a good recognition rate at 94.12% which is better than related work.