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FacetModalReconginationvinImage Processing
+systems+aim+to+enhance+the+capability+of+face+recognition+in+challenging+cross-modal+authentication+scenarios.+However%252C+the+significant+domain+gap+between+the+source+and+target+modalities+poses+a+considerable+challenge+for+cross-domain+matching.+Existing+literature+primarily+focuses+on+developing+HFR+approaches+for+specific+pairs+of+face+modalities%252C+necessitating+the+explicit+training+of+models+for+each+source-target+combination.+In+this+work%252C+we+introduce+a+novel+framework+designed+to+train+a+modality-agnostic+HFR+method+capable+of+handling+multiple+modalities+during+inference%252C+all+without+explicit+knowledge+of+the+target+modality+labels.+We+achieve+this+by+implementing+a+computationally+efficient+automatic+routing+mechanism+called+Switch+Style+Modulation+Blocks+(SSMB)+that+trains+various+domain+expert+modulators+which+transform+the+feature+maps+adaptively+reducing+the+domain+gap.+Our+proposed+SSMB+can+be+trained+end-to-end+and+seamlessly+integrated+into+pre-trained+face+recognition+models%252C+transforming+them+into+modality-agnostic+HFR+models.+We+have+performed+extensive+evaluations+on+HFR+benchmark+datasets+to+demonstrate+its+effectiveness.+The+source+code+and+protocols+will+be+made+publicly+available.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.42f39d1cb0aead1b55644fe74eacec05%3frik%3d2EjxLTsZaqf4Mg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Facet+Modal+Reconginationv+in+Image+Processing&FORM=IRPRST&ck=BB33B0BF13ED45A6A3850E88AEDE56A7&selectedIndex=20&itb=0)
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