Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : PCA

Visual Depression Diagnosis From Face Based on Various Classification Algorithms

Sana A. Nasser; Ivan A. Hashim; Wisam H. Ali

Engineering and Technology Journal, 2020, Volume 38, Issue 11, Pages 1717-1729
DOI: 10.30684/etj.v38i11A.1714

Most psychologists believe that facial behavior through depression differs from facial behavior in the absence of depression, so facial behavior can be utilized as a dependable indicator for spotting depression. Visual depression diagnosis system (VDD) establishes dependents on expressions of the face that are expense-effective and movable. At this work, the VDD system is designed according to the Facial Action Coding System (FACS) to extract features of the face. The key concept of the Facial Action Coding System (FACS) to explain the whole face behavior utilizing Action Units (AUs), every AU is linked to the motion of unique or maybe further face muscles. Six AUs have utilized as depression features; those action units are AUs 4, 5, 6, 7, 10, and 12. The datasets that employed to evaluate the performance of the proposed system are gathered for 125 participants (30 males, 95 females); many of them are among 17-60 years of age. At the final step of the current system, four kinds of classification techniques were applied separately; those classifiers algorithms are KNN, SVM, PCA, and LDA. The outcomes of the simulation indicate that the best outcomes are achieved utilizing the KNN and LDA classifiers, where the success rate is 85%. New classification methods in the VDD system are the key contributions of this research, gather real databases that can utilize to compute the performance of every other VDD system based on face emotions, and choose appropriate features of the face.

Effect Of Eigenfaces Level On The Face Recognition Rate Using Principal Component Analysis

Eyad. I. Abbas

Engineering and Technology Journal, 2015, Volume 33, Issue 3, Pages 729-737

This paper presents an approach to study the effect of the different eigenfaces levels on the faces recognition rate using principal component analysis. The increase in the strength of the variables and the lighting in the facial geometry to represent the human face , has been using the principal component analysis (PCA) on the image of the whole face . The principal component analysis is a statistical measurement method , which works in the field of linear and can be used to reduce the dimensions of the image and thus serve to reduce the calculations significantly to the image database . It is a method gives better accuracy and a higher rate of recognition . The experiment was conducted on 50 images from the database of faces (ORL), using 40 images for the training set and 15 images for the test group ( five images in common with the training set and the remaining 10 images are different in expression and corner ) . The results proved that the proposed method is effective and successful in obtaining recognition rate up to 100% in the third level when using ten eigenfaces.

Principal Component Analysis Based Wavelet Transform

Hana; a M. Salman

Engineering and Technology Journal, 2012, Volume 30, Issue 9, Pages 1538-1549

The principal component analysis (PCA) is a valuable statistical means,
implemented in time domain that has found application in many fields such as face
recognition and image compression, and is a common technique for finding patterns in
data of high dimension. This paper investigates the ability to implement PCA in
frequency domain, by using the wavelet transform (WT), and evaluate its effectiveness
based on face recognition as a means to find patterns in data. The basic idea of
frequency domain implementation of the PCA refers to the correlation
implementation using wavelet transform.
The Min-max is invoked to increase wavelet based eigenface robustness to
variations in facial geometry and illumination. Two face images are contrast in terms
of their correlation distance. A threshold is used to restrict the impostor face image
from being identified. Experimental results point up the effectiveness of a new method
in either using varying (noisy images, unknown images, face expressions, illumine,
and scales ).