DIC Structural HMM based IWAK-means to Enclosed Face Data

Abstract

This paper identifies two novel techniques for face features extraction based on two different multi-resolution analysis tools; the first called curvelet transform while the second is waveatom transform. The resultant features are trained and tested via three improved hidden Markov Model (HMM) classifiers, such as: Structural HMM (SHMM), Deviance Information CriterionInverse Weighted Average K-mean-SHMM (DIC-IWAK-SHMM), and Enclosed Model Selection Criterion (EMC) coupled with DIC-IWAK-SHMM as the proposed methods for face recognition.A comparative studies for DIC-IWAK-SHMM approach to recognize the face ware achieved by using two type of features; one method using Waveatom features and the other method uses 2-level Curvelet features, these two methods compared with a six methods that used in previous researches.The goal of the paper is twofold; using Deviance information criterion and IWAK-means clustering algorithm based on SHMM.

Integrating Bat Algorithm to Inverse Weighted K-means

“…It is introduced to solve the initial starting centroids position in the K-means clustering algorithm by using two modifications to the original K-means clustering algorithm in calculating the cost function [10]. The first one is using the sum of the inverse of distances from a specific data point to every centroid, and the second one is multiplication the inverse sum by weight which is the minimum distance of the same data point to the centroids as shown in ( 5) for the cost function.…”