IEEE Trans Pattern Anal Mach Intell - Facial Age Estimation by Learning from Label Distributions.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ featur(1941) imag(1645) propos(1176) }
{ age(1611) year(1155) adult(843) }
{ estim(2440) model(1874) function(577) }
{ general(901) number(790) one(736) }
{ process(1125) use(805) approach(778) }
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{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ studi(1410) differ(1259) use(1210) }
{ method(1557) propos(1049) approach(1037) }
{ data(2317) use(1299) case(1017) }
{ first(2504) two(1366) second(1323) }
{ can(981) present(881) function(850) }
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{ concept(1167) ontolog(924) domain(897) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

One of the main difficulties in facial age estimation is that the learning algorithms cannot expect sufficient and complete training data. Fortunately, the faces at close ages look quite similar since aging is a slow and smooth process. Inspired by this observation, instead of considering each face image as an instance with one label (age), this paper regards each face image as an instance associated with a label distribution. The label distribution covers a certain number of class labels, representing the degree that each label describes the instance. Through this way, one face image can contribute to not only the learning of its chronological age, but also the learning of its adjacent ages. Two algorithms named IIS-LLD and CPNN are proposed to learn from such label distributions. Experimental results on two aging face databases show remarkable advantages of the proposed label distribution learning algorithms over the compared single-label learning algorithms, either specially designed for age estimation or for general purpose.

Resumo Limpo

one main difficulti facial age estim learn algorithm expect suffici complet train data fortun face close age look quit similar sinc age slow smooth process inspir observ instead consid face imag instanc one label age paper regard face imag instanc associ label distribut label distribut cover certain number class label repres degre label describ instanc way one face imag can contribut learn chronolog age also learn adjac age two algorithm name iislld cpnn propos learn label distribut experiment result two age face databas show remark advantag propos label distribut learn algorithm compar singlelabel learn algorithm either special design age estim general purpos

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