{"id":5904,"date":"2025-11-10T00:00:00","date_gmt":"2025-11-09T22:00:00","guid":{"rendered":"https:\/\/elmeistrai.lt\/?p=5904"},"modified":"2025-10-07T14:57:24","modified_gmt":"2025-10-07T11:57:24","slug":"veido-atpazinimo-algoritmu-veikimas","status":"publish","type":"post","link":"https:\/\/elmeistrai.lt\/es\/veido-atpazinimo-algoritmu-veikimas\/","title":{"rendered":"Veido atpa\u017einimo algoritm\u0173 veikimas"},"content":{"rendered":"

Kaip j\u016bs\u0173 telefonas atpa\u017e\u012fsta j\u016bs\u0173 veid\u0105 grei\u010diau nei geriausia draug\u0117<\/h2>\n\n\n\n

Prisimenu, kai pirm\u0105 kart\u0105 i\u0161band\u017eiau veido atpa\u017einimo funkcij\u0105 savo telefone \u2013 atrod\u0117 kaip ma\u017eas stebuklas. Tiesiog pa\u017ei\u016bri \u012f ekran\u0105 ir \u012frenginys atsirakina. Bet kaip tas dalykas i\u0161 tikr\u0173j\u0173 veikia? Pasirodo, u\u017e \u0161ito paprasto veiksmo slypi sud\u0117tinga technologija, kuri per kelias de\u0161imtmetes evoliucionavo nuo mokslini\u0173 eksperiment\u0173 iki kasdienio \u012frankio m\u016bs\u0173 ki\u0161en\u0117se.<\/p>\n\n\n\n

Veido atpa\u017einimo technologija \u0161iandien yra visur \u2013 nuo telefon\u0173 iki oro uost\u0173, nuo socialini\u0173 tinkl\u0173 iki bank\u0173 sistem\u0173. Ir nors daugelis \u017emoni\u0173 naudojasi ja kasdien, nedaugelis supranta, kokie algoritmai ir procesai vyksta u\u017e ekrano. Pabandykime tai i\u0161siai\u0161kinti be sud\u0117ting\u0173 matematini\u0173 formuli\u0173 ir techninio \u017eargono.<\/p>\n\n\n\n

Nuo nuotraukos iki skaitmenin\u0117s veido \u017eem\u0117lapio<\/h2>\n\n\n\n

Pirmasis ir svarbiausias \u017eingsnis \u2013 tai kaip kompiuteris „mato” j\u016bs\u0173 veid\u0105. Skirtingai nei mes, kurie tiesiog atpa\u017e\u012fstame veidus intuityviai, kompiuteriui reikia visk\u0105 paversti skai\u010diais. \u010cia ir prasideda magija.<\/p>\n\n\n\n

Kai kamera u\u017efiksuoja j\u016bs\u0173 veid\u0105, sistema pirmiausia turi j\u012f aptikti vaizde. Tai gali skamb\u0117ti paprasta, bet pagalvokite \u2013 nuotraukoje gali b\u016bti daug objekt\u0173, skirtingas ap\u0161vietimas, \u012fvair\u016bs kampai. Modern\u016bs algoritmai naudoja tai, kas vadinama „Haar kaskadais” arba gilesnius neuroninius tinklus, kad atskirt\u0173 veid\u0105 nuo fono.<\/p>\n\n\n\n

Aptik\u0119s veid\u0105, algoritmas pradeda ie\u0161koti tam tikr\u0173 charaktering\u0173 ta\u0161k\u0173 \u2013 tai vadiname „veido orientyrais” arba landmark detection. Paprastai sistema ie\u0161ko 68-194 specifini\u0173 ta\u0161k\u0173: aki\u0173 kampu\u010di\u0173, nosies galiuko, l\u016bp\u0173 kont\u016br\u0173, \u017eandikauli\u0173 linij\u0173. Kai kurios pa\u017eangesn\u0117s sistemos gali identifikuoti net iki 1000 toki\u0173 ta\u0161k\u0173. Kuo daugiau ta\u0161k\u0173, tuo tikslesn\u0117 identifikacija.<\/p>\n\n\n\n

Geometrija ir atstum\u0173 \u017eaidimas<\/h3>\n\n\n\n

Dabar prasideda \u012fdomiausia dalis. Sistema i\u0161matuoja atstumus tarp \u0161i\u0173 ta\u0161k\u0173 ir sukuria tai, kas vadinama „veido para\u0161u” arba faceprint. Tai tarsi j\u016bs\u0173 veido skaitmeninis pir\u0161t\u0173 atspaudas. Pavyzd\u017eiui, atstumas tarp aki\u0173, nosies ilgis, skruostikauli\u0173 plotis, \u017eandikaulio forma \u2013 visa tai paver\u010diama matematine formule.<\/p>\n\n\n\n

\u012edomu tai, kad \u0161is „para\u0161as” paprastai u\u017eima labai ma\u017eai vietos \u2013 da\u017eniausiai tik kelias kilobaites. J\u016bs\u0173 telefone nesaugoma tikra nuotrauka (bent jau teori\u0161kai), o tik \u0161is matematinis kodas. Tod\u0117l net jei kas nors gaut\u0173 prieig\u0105 prie \u0161i\u0173 duomen\u0173, jis nematyt\u0173 j\u016bs\u0173 veido nuotraukos, o tik skai\u010di\u0173 rinkin\u012f.<\/p>\n\n\n\n

Dirbtinio intelekto revoliucija veido atpa\u017einime<\/h2>\n\n\n\n

Ankstyvosios veido atpa\u017einimo sistemos buvo gana primityvios ir veik\u0117 remiantis paprastomis taisykl\u0117mis. Bet ma\u017edaug prie\u0161 10-15 met\u0173 viskas pasikeit\u0117, kai \u012f \u017eaidim\u0105 \u012fsijung\u0117 gilieji neuroniniai tinklai (deep learning). Tai buvo tikras prover\u017eis.<\/p>\n\n\n\n

\u0160iuolaikin\u0117s sistemos naudoja konvoliucinius neuroninius tinklus (CNN), kurie mokosi atpa\u017einti veidus pana\u0161iai kaip \u017emogaus smegenys \u2013 per patirt\u012f. Sistema „maitinama” milijonais veido nuotrauk\u0173, ir ji pati i\u0161moksta, kokie po\u017eymiai yra svarbiausi atpa\u017einimui. Nebereikia programuotojams rankiniu b\u016bdu nurodyti, kad svarbu matuoti atstum\u0105 tarp aki\u0173 \u2013 sistema pati tai supranta.<\/p>\n\n\n\n

Vienas populiariausi\u0173 toki\u0173 tinkl\u0173 vadinasi FaceNet, kur\u012f suk\u016br\u0117 Google in\u017einieriai. Jis gali atpa\u017einti veidus su daugiau nei 99% tikslumu \u2013 tai geriau nei vidutinis \u017emogus! Kiti \u017einomi algoritmai \u2013 DeepFace (Facebook\/Meta), VGGFace, ArcFace. Kiekvienas turi savo ypatum\u0173, bet principas pana\u0161us.<\/p>\n\n\n\n

Kaip sistema mokosi<\/h3>\n\n\n\n

Mokymo procesas yra fascinuojantis. \u012esivaizduokite, kad turite milijon\u0105 nuotrauk\u0173 su 10,000 skirting\u0173 \u017emoni\u0173. Sistema pradeda mokytis, palyginant nuotraukas ir bandydama suprasti, kas daro kiekvien\u0105 veid\u0105 unikal\u0173. Ji mokosi taip, kad to paties \u017emogaus skirtingos nuotraukos (su skirtingomis \u0161ukuosenomis, ap\u0161vietimu, kampais) b\u016bt\u0173 atpa\u017e\u012fstamos kaip tas pats asmuo, o skirting\u0173 \u017emoni\u0173 nuotraukos b\u016bt\u0173 ai\u0161kiai atskirtos.<\/p>\n\n\n\n

\u010cia naudojamas metodas vadinamas „triplet loss” \u2013 sistema vienu metu \u017ei\u016bri \u012f tris nuotraukas: bazin\u0119, tos pa\u010dios asmenyb\u0117s ir kito \u017emogaus. Ji mokosi taip koreguoti savo parametrus, kad pirmosios dvi b\u016bt\u0173 „artimesn\u0117s” matematine prasme nei pirmoji ir tre\u010dioji.<\/p>\n\n\n\n

Kod\u0117l j\u016bs\u0173 telefonas nesupainioja j\u016bs\u0173 su dvyniu<\/h2>\n\n\n\n

Vienas da\u017eniausi\u0173 klausim\u0173 \u2013 kaip sistema atskiria labai pana\u0161ius veidus? Atsakymas slypi detal\u0117se. Nors mes galime pamatyti du pana\u0161ius veidus ir pasakyti „jie atrodo beveik vienodai”, algoritmai mato mikroskopines detales, kuri\u0173 mes nepastebime.<\/p>\n\n\n\n

\u0160iuolaikin\u0117s sistemos analizuoja ne tik akivaizd\u017eius bruo\u017eus, bet ir subtilias detales: odos tekst\u016br\u0105, smulkias rauk\u0161les, net kraujagysli\u0173 i\u0161sid\u0117stym\u0105 po oda (naudojant infraraudon\u0173j\u0173 spinduli\u0173 kamer\u0105). Kai kurios sistemos gali aptikti net mikro-i\u0161rai\u0161kas \u2013 nevalingus veido raumen\u0173 judesius, kurie yra unikal\u016bs kiekvienam \u017emogui.<\/p>\n\n\n\n

Pavyzd\u017eiui, Apple Face ID naudoja daugiau nei 30,000 infraraudon\u0173j\u0173 ta\u0161k\u0173 projekcij\u0105 ant j\u016bs\u0173 veido, sukurdama tiksl\u0173 3D \u017eem\u0117lap\u012f. Tai leid\u017eia sistemai atpa\u017einti jus net tamsoje ir atskirti tikr\u0105 veid\u0105 nuo nuotraukos.<\/p>\n\n\n\n

Saugumo i\u0161\u0161\u016bkiai ir apgaul\u0117s metodai<\/h2>\n\n\n\n

\u017dinoma, kur technologija, ten ir bandymai j\u0105 apgauti. Ankstyv\u0105sias veido atpa\u017einimo sistemas buvo galima apgauti tiesiog parod\u017eius nuotrauk\u0105. Dabar tai jau nebeveikia su rimtomis sistemomis, bet suk\u010diai nuolat ie\u0161ko nauj\u0173 b\u016bd\u0173.<\/p>\n\n\n\n

Vienas \u017einomiausi\u0173 bandym\u0173 \u2013 naudoti 3D atspausdint\u0105 veido kauk\u0119. Kai kurios sistemos i\u0161 tikr\u0173j\u0173 gali b\u016bti apgautos labai kokybi\u0161komis kauk\u0117mis, kurios kainuoja t\u016bkstan\u010dius eur\u0173. Bet prakti\u0161kai tai n\u0117ra reali gr\u0117sm\u0117 eiliniam vartotojui \u2013 kam nors reik\u0117t\u0173 tur\u0117ti j\u016bs\u0173 veido 3D skenavim\u0105 ir investuoti daug pinig\u0173 bei laiko.<\/p>\n\n\n\n

Modernios sistemos naudoja „gyvumo aptikim\u0105” (liveness detection). Jos gali papra\u0161yti j\u016bs\u0173 mirks\u0117ti, \u0161ypsotis, pasukti galv\u0105 arba tiesiog analizuoja mikro-judesius, kurie \u012frodo, kad prie\u0161 kamer\u0105 yra gyvas \u017emogus. Kai kurios sistemos naudoja termografij\u0105 \u2013 matuoja \u0161ilum\u0105, kuri\u0105 skleid\u017eia gyvas veidas.<\/p>\n\n\n\n

Privatumo klausimas<\/h3>\n\n\n\n

\u010cia prasideda filosofin\u0117 diskusija. Kiekvienas veido atpa\u017einimo naudojimas kelia klausim\u0105: kur saugomi mano duomenys? Kas juos mato? Ar galiu b\u016bti sekamas be savo \u017einios?<\/p>\n\n\n\n

Geros naujienos yra tai, kad daugelis \u0161iuolaikini\u0173 telefon\u0173 (kaip iPhone ar naujesni Android \u012frenginiai) apdoroja veido duomenis tiesiog pa\u010diame \u012frenginyje, nenusi\u0173sdami j\u0173 \u012f debes\u012f. J\u016bs\u0173 veido „para\u0161as” saugomas u\u017e\u0161ifruotame telefono \u010dipe, kur\u012f beveik ne\u012fmanoma pasiekti net su fizine prieiga prie \u012frenginio.<\/p>\n\n\n\n

Ta\u010diau situacija kitokia su vie\u0161omis sistemomis \u2013 oro uostuose, parduotuv\u0117se, gatv\u0117se. \u010cia duomenys da\u017enai saugomi centralizuotose duomen\u0173 baz\u0117se, ir reguliavimas skiriasi priklausomai nuo \u0161alies. Europoje GDPR \u012fstatymai suteikia tam tikr\u0105 apsaug\u0105, bet ne visur pasaulyje yra tokios grie\u017etos taisykl\u0117s.<\/p>\n\n\n\n

Praktinis panaudojimas \u0161iandien ir rytoj<\/h2>\n\n\n\n

Veido atpa\u017einimas jau seniai per\u017eeng\u0117 telefono atrakinimo ribas. Bankai naudoja j\u012f kliento tapatyb\u0117s patvirtinimui, oro uostuose jis paspartina pasienio kontrol\u0119, parduotuv\u0117s analizuoja pirk\u0117j\u0173 demografij\u0105 (nors tai ir keliama diskusij\u0173).<\/p>\n\n\n\n

Medicinos srityje veido atpa\u017einimas padeda diagnozuoti retas genetines ligas \u2013 kai kurios b\u016bkl\u0117s pasirei\u0161kia specifiniais veido bruo\u017eais, kuriuos algoritmai gali aptikti grei\u010diau nei gydytojai. Yra tyrim\u0173, kurie rodo, kad dirbtinis intelektas gali net nustatyti tam tikras \u0161irdies ligas analizuodamas veido spalv\u0105 ir mikro-poky\u010dius.<\/p>\n\n\n\n

\u0160vietimo \u012fstaigose sistema gali automati\u0161kai registruoti lankomum\u0105. Renginiuose \u2013 greitinti \u012f\u0117jim\u0105 be biliet\u0173 tikrinimo. Namuose \u2013 personalizuoti i\u0161mani\u0173j\u0173 nam\u0173 nustatymus priklausomai nuo to, kas \u012f\u0117jo \u012f kambar\u012f.<\/p>\n\n\n\n

Kas laukia ateityje<\/h3>\n\n\n\n

Technologija nestovi vietoje. Jau dabar kuriami algoritmai, kurie gali atpa\u017einti \u017emones net su kauk\u0117mis (COVID pandemija tai paspartino), i\u0161 labai dideli\u0173 atstum\u0173, net i\u0161 dalinio veido profilio. Kai kurie tyrimai rodo, kad ateityje bus \u012fmanoma atpa\u017einti \u017emones net pagal j\u0173 eisenos b\u016bd\u0105 ar k\u016bno kalb\u0105, net nematant veido.<\/p>\n\n\n\n

Kita kryptis \u2013 emocij\u0173 atpa\u017einimas. Algoritmai mokosi suprasti, ar \u017emogus yra laimingas, li\u016bdnas, piktas ar sunerim\u0119s. Tai gali b\u016bti naudinga psichologijoje, rinkodaroje, net automobili\u0173 saugume (aptikti, ar vairuotojas yra pavarg\u0119s ar i\u0161sibla\u0161k\u0119s).<\/p>\n\n\n\n

K\u0105 reikia \u017einoti naudojant veido atpa\u017einim\u0105<\/h2>\n\n\n\n

Jei naudojate veido atpa\u017einim\u0105 savo telefone ar kituose \u012frenginiuose, yra keletas dalyk\u0173, kuriuos verta \u017einoti. Pirma, sistema veikia geriau, kai j\u0105 „mokote” skirtingomis s\u0105lygomis \u2013 su akiniais ir be j\u0173, skirtingu ap\u0161vietimu, su skirtingomis \u0161ukuosenomis.<\/p>\n\n\n\n

Antra, jei labai susir\u016bpin\u0119s saugumu, \u017einok, kad veido atpa\u017einimas n\u0117ra 100% ne\u012fveikiamas. Kai kurie ekspertai rekomenduoja naudoti papildom\u0105 apsaug\u0105 \u2013 PIN kod\u0105 ar pir\u0161t\u0173 atspaudus \u2013 ypa\u010d svarbiems \u012frenginiams. Yra situacij\u0173 (pavyzd\u017eiui, kai miegojate), kai kas nors gal\u0117t\u0173 atrakinti j\u016bs\u0173 telefon\u0105 tiesiog priartin\u0119s j\u012f prie j\u016bs\u0173 veido.<\/p>\n\n\n\n

Tre\u010dia, b\u016bkite atsarg\u016bs su program\u0117l\u0117mis, kurios pra\u0161o prieigos prie kameros ir veido atpa\u017einimo funkcij\u0173. Ne visos program\u0117l\u0117s yra patikimos, ir kai kurios gali piktnaud\u017eiauti \u0161ia informacija. Skaitykite privatumo politikas ir suteikite leidimus tik patikimoms program\u0117l\u0117ms.<\/p>\n\n\n\n

Kai technologija sutinka \u017emoni\u0161kum\u0105<\/h2>\n\n\n\n

Gr\u012f\u017etant prie prad\u017eios \u2013 veido atpa\u017einimo technologija yra nuostabus pavyzdys, kaip matematika, kompiuteri\u0173 mokslas ir dirbtinis intelektas gali sukurti ka\u017ek\u0105, kas atrodo beveik magi\u0161kai. Bet svarbu prisiminti, kad tai tik \u012frankis, kurio vert\u0117 priklauso nuo to, kaip j\u012f naudojame.<\/p>\n\n\n\n

Geriausi veido atpa\u017einimo algoritmai \u0161iandien dirba su tikslumu, kuris pranoksta \u017emogaus geb\u0117jimus. Ta\u010diau jie vis dar turi apribojim\u0173 \u2013 gali b\u016bti \u0161ali\u0161ki (kai kurie algoritmai pras\u010diau atpa\u017e\u012fsta tam tikr\u0173 etnini\u0173 grupi\u0173 veidus, jei nebuvo treniruoti su pakankamai \u012fvairiomis nuotraukomis), gali klysti d\u0117l ne\u012fprast\u0173 s\u0105lyg\u0173, ir svarbiausia \u2013 jiems tr\u016bksta konteksto supratimo, kur\u012f mes, \u017emon\u0117s, turime nat\u016braliai.<\/p>\n\n\n\n

Ateityje tik\u0117tina, kad \u0161i technologija taps dar tikslesn\u0117, greitesn\u0117 ir labiau paplitusi. M\u016bs\u0173 u\u017eduotis \u2013 u\u017etikrinti, kad ji b\u016bt\u0173 naudojama eti\u0161kai, skaidriai ir su pagarba kiekvieno \u017emogaus privatumui. Technologija tur\u0117t\u0173 tarnauti \u017emon\u0117ms, o ne atvirk\u0161\u010diai. Ir kol mes tai prisimename, veido atpa\u017einimas gali b\u016bti puikus \u012frankis, darantis m\u016bs\u0173 gyvenim\u0105 saugesn\u012f ir patogesn\u012f.<\/p>","protected":false},"excerpt":{"rendered":"

Kaip j\u016bs\u0173 telefonas atpa\u017e\u012fsta j\u016bs\u0173 veid\u0105 grei\u010diau nei geriausia draug\u0117 Prisimenu, kai pirm\u0105 kart\u0105 i\u0161band\u017eiau veido atpa\u017einimo funkcij\u0105 savo telefone…<\/p>","protected":false},"author":1,"featured_media":5903,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-5904","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-kompiuteriu-remontas-kompiuteriai-naujienos-patarimai","wpcat-7-id"],"yoast_head":"\nVeido atpa\u017einimo algoritm\u0173 veikimas<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/elmeistrai.lt\/es\/veido-atpazinimo-algoritmu-veikimas\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" 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