The Application of Modified Least Trimmed Squares with Genetic Algorithms Method in Face Recognition
Authors: Azimah Abdul Rahim Nur , Azura Md. Ghani Nor , Mohamed Norazan , Hashim Hishamuddin , Musirin Ismail 
Published: January 2019

Severely occluded face images are the main problem in low performance of face recognition algorithms. In this paper, we apply a new algorithm, a modified version of the least trimmed squares (LTS) with a genetic algorithms introduce by <!--[if supportFields]><i><span

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EN-US;mso-bidi-language:AR-SA'><span style='mso-element:field-begin'></span><span

style='mso-spacerun:yes'> </span>ADDIN EN.CITE

&lt;EndNote&gt;&lt;Cite&gt;&lt;Author&gt;Rahim

Abdul&lt;/Author&gt;&lt;Year&gt;2016&lt;/Year&gt;&lt;RecNum&gt;303&lt;/RecNum&gt;&lt;DisplayText&gt;[1]&lt;/DisplayText&gt;&lt;record&gt;&lt;rec-number&gt;303&lt;/rec-number&gt;&lt;foreign-keys&gt;&lt;key

app=&quot;EN&quot; db-id=&quot;f5s5xeavm025z9expeavfwr2x2szra2tx55s&quot;

timestamp=&quot;1507074147&quot;&gt;303&lt;/key&gt;&lt;/foreign-keys&gt;&lt;ref-type

name=&quot;Journal

Article&quot;&gt;17&lt;/ref-type&gt;&lt;contributors&gt;&lt;authors&gt;&lt;author&gt;Rahim

Abdul, Nur Azimah&lt;/author&gt;&lt;author&gt;Ramli, Norazan

Mohamed&lt;/author&gt;&lt;author&gt;Md Ghani, Nor Azura&lt;/author&gt;&lt;/authors&gt;&lt;/contributors&gt;&lt;titles&gt;&lt;title&gt;The

Performance of Modified Least Trimmed Squares-Based Methods for Large Data Sets

Based on Monte Carlo Simulations&lt;/title&gt;&lt;secondary-title&gt;Advanced

Science Letters&lt;/secondary-title&gt;&lt;/titles&gt;&lt;periodical&gt;&lt;full-title&gt;Advanced

Science

Letters&lt;/full-title&gt;&lt;/periodical&gt;&lt;pages&gt;4359-4363&lt;/pages&gt;&lt;volume&gt;22&lt;/volume&gt;&lt;number&gt;12&lt;/number&gt;&lt;keywords&gt;&lt;keyword&gt;Genetic

Algorithm&lt;/keyword&gt;&lt;keyword&gt;Large Data&lt;/keyword&gt;&lt;keyword&gt;Least

Trimmed Squares&lt;/keyword&gt;&lt;/keywords&gt;&lt;dates&gt;&lt;year&gt;2016&lt;/year&gt;&lt;pub-dates&gt;&lt;date&gt;//&lt;/date&gt;&lt;/pub-dates&gt;&lt;/dates&gt;&lt;urls&gt;&lt;related-urls&gt;&lt;url&gt;http://www.ingentaconnect.com/content/asp/asl/2016/00000022/00000012/art00091&lt;/url&gt;&lt;url&gt;https://doi.org/10.1166/asl.2016.8155&lt;/url&gt;&lt;/related-urls&gt;&lt;/urls&gt;&lt;electronic-resource-num&gt;10.1166/asl.2016.8155&lt;/electronic-resource-num&gt;&lt;/record&gt;&lt;/Cite&gt;&lt;/EndNote&gt;<span

style='mso-element:field-separator'></span></span></i><![endif]-->[1]<!--[if supportFields]><i><span

style='font-size:9.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family:

"Times New Roman";color:black;mso-ansi-language:EN-US;mso-fareast-language:

EN-US;mso-bidi-language:AR-SA'><span style='mso-element:field-end'></span></span></i><![endif]-->. We focused on the application of modified LTS with genetic algorithm method for face image recognition. This algorithm uses genetic algorithms to construct a basic subset rather than selecting the basic subset randomly. The modification in this method lessens the number of trials to obtain the minimum of the LTS objective function. This method was then applied to two benchmark datasets with clean and occluded query images. The performance of this method was measured by recognition rates. The AT&amp;T dataset and Yale Dataset with different image pixel sizes were used to assess the method in performing face recognition. The query images were contaminated with salt and pepper noise. The modified LTS with GAs method is applied in face recognition framework by using the contaminated images as query image in the context of linear regression. By the end of this study, we can determine this either this method can perform well in dealing with occluded images or vice versa.