Abstract
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
 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-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.
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