Genetic Algorithms Applied in Face Recognition

2012 ◽  
Vol 10 (6) ◽  
pp. 2280-2285 ◽  
Author(s):  
Luciano Xavier Medeiros ◽  
Gilberto Arantes Carrijo ◽  
Edna Lucia Flores ◽  
Antonio C Paschoarelli Veiga
Sensors ◽  
2015 ◽  
Vol 15 (8) ◽  
pp. 17944-17962 ◽  
Author(s):  
Gabriel Hermosilla ◽  
Francisco Gallardo ◽  
Gonzalo Farias ◽  
Cesar Martin

Author(s):  
Nur Azimah Abdul Rahim ◽  
Nor Azura Md. Ghani ◽  
Norazan Mohamed ◽  
Hishamuddin Hashim ◽  
Ismail Musirin

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.


2010 ◽  
Vol 69 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Jisien Yang ◽  
Adrian Schwaninger

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.


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