The use of hidden Markov models to verify the identity based on facial asymmetry

Abstract

This work concerns the use of biometric features, resulting from the look of a face, for the verification purposes. Different methods of selection and feature analysis during face recognition are presented here. The description contains mainly analysis possibilities and also identity verification based on asymmetric facial features-in later stages. The new verification method has been introduced based on designated characteristic points. These points were designated through appropriate coded information about facial asymmetry as observation vectors and recognition using hidden Markov models. The advantage of these models is that we can use vectors of different lengths. Such vectors appear in incomplete data when all points cannot be located.

Psoriasis Skin Disease Identification Using Support Vector Machine(SVM) Image Classification and Determining the Growth Rate

“…SVM uses a way known kernel trick to transfigure the data and then based on that it detects an optimal boundary between the optimal outputs. The technique does extremely complex data transformations, then figures out a way to separate data based on the outputs defined [6,11] each of the segmented images is bifurcated into nine parts for easier identification of the diseased region [13,16]. Based on the bifurcation process, relativity nature of the images are obtained.…”