人臉特征點(diǎn)檢測是人臉檢測過程中的一個重要環(huán)節(jié)。以往我們采用的方法是OpenCV或者Dlib，雖然Dlib優(yōu)于OpenCV，但是檢測出的68個點(diǎn)并沒有覆蓋額頭區(qū)域。Reddit一位網(wǎng)友便在此基礎(chǔ)上做了進(jìn)一步研究，能夠檢測出81個面部特征點(diǎn)，使得準(zhǔn)確度有所提高。

或許，這就是你需要的人臉特征點(diǎn)檢測方法。

人臉特征點(diǎn)檢測(Facial landmark detection)是人臉檢測過程中的一個重要環(huán)節(jié)。是在人臉檢測的基礎(chǔ)上進(jìn)行的，對人臉上的特征點(diǎn)例如嘴角、眼角等進(jìn)行定位。

近日，Reddit一位網(wǎng)友po出一個帖子，表示想與社區(qū)同胞們分享自己的一點(diǎn)研究成果：

其主要的工作就是在人臉檢測Dlib庫68個特征點(diǎn)的基礎(chǔ)上，增加了13個特征點(diǎn)(共81個)，使得頭部檢測和圖像操作更加精確。

現(xiàn)在來看一下demo：

demo視頻鏈接：

https://www.youtube.com/watch?v=mDJrASIB1T0

81個特征點(diǎn)，人臉特征點(diǎn)檢測更加精準(zhǔn)

以往我們在做人臉特征點(diǎn)檢測的時候，通常會用OpenCV來進(jìn)行操作。

但自從人臉檢測Dlib庫問世，網(wǎng)友們紛紛表示：好用！Dlib≥OpenCV！Dlib具有更多的人臉識別模型，可以檢測臉部68甚至更多的特征點(diǎn)。

我們來看一下Dlib的效果：

Dlib人臉特征點(diǎn)檢測效果圖

那么這68個特征點(diǎn)又是如何分布的呢？請看下面這張“面相圖”：

人臉68個特征點(diǎn)分布

但無論是效果圖和“面相圖”，我們都可以發(fā)現(xiàn)在額頭區(qū)域是沒有分布特征點(diǎn)的。

于是，網(wǎng)友便提出了一個特征點(diǎn)能夠覆蓋額頭區(qū)域的模型。

該模型是一個自定義形狀預(yù)測模型，在經(jīng)過訓(xùn)練后，可以找到任何給定圖像中的81個面部特征點(diǎn)。

它的訓(xùn)練方法類似于Dlib的68個面部特征點(diǎn)形狀預(yù)測器。只是在原有的68個特征點(diǎn)的基礎(chǔ)上，在額頭區(qū)域增加了13個點(diǎn)。這就使得頭部的檢測，以及用于需要沿著頭部頂部的點(diǎn)的圖像操作更加精準(zhǔn)。

81個特征點(diǎn)效果圖

這13個額外的特征點(diǎn)提取的方法，是根據(jù)該博主之前的工作完成的。

GitHub地址：

https://github.com/codeniko/eos

該博主繼續(xù)使用Surrey Face Model，并記下了他認(rèn)為適合他工作的13個點(diǎn)，并做了一些細(xì)節(jié)的修改。

當(dāng)然，博主還慷慨的分享了訓(xùn)練的代碼：

1#!/usr/bin/python 2#Thecontentsofthisfileareinthepublicdomain.SeeLICENSE_FOR_EXAMPLE_PROGRAMS.txt 3# 4#Thisexampleprogramshowshowtousedlib'simplementationofthepaper: 5#OneMillisecondFaceAlignmentwithanEnsembleofRegressionTreesby 6#VahidKazemiandJosephineSullivan,CVPR2014 7# 8#Inparticular,wewilltrainafacelandmarkingmodelbasedonasmall 9#datasetandthenevaluateit.Ifyouwanttovisualizetheoutputofthe 10#trainedmodelonsomeimagesthenyoucanrunthe 11#face_landmark_detection.pyexampleprogramwithpredictor.datastheinput 12#model. 13# 14#Itshouldalsobenotedthatthiskindofmodel,whileoftenusedforface 15#landmarking,isquitegeneralandcanbeusedforavarietyofshape 16#predictiontasks.Butherewedemonstrateitonlyonasimpleface 17#landmarkingtask. 18# 19#COMPILING/INSTALLINGTHEDLIBPYTHONINTERFACE 20#Youcaninstalldlibusingthecommand: 21#pipinstalldlib 22# 23#Alternatively,ifyouwanttocompiledlibyourselfthengointothedlib 24#rootfolderandrun: 25#pythonsetup.pyinstall 26# 27#Compilingdlibshouldworkonanyoperatingsystemsolongasyouhave 28#CMakeinstalled.OnUbuntu,thiscanbedoneeasilybyrunningthe 29#command: 30#sudoapt-getinstallcmake 31# 32#AlsonotethatthisexamplerequiresNumpywhichcanbeinstalled 33#viathecommand: 34#pipinstallnumpy 35 36importos 37importsys 38importglob 39 40importdlib 41 42#Inthisexamplewearegoingtotrainafacedetectorbasedonthesmall 43#facesdatasetintheexamples/facesdirectory.Thismeansyouneedtosupply 44#thepathtothisfacesfolderasacommandlineargumentsowewillknow 45#whereitis. 46iflen(sys.argv)!=2: 47print( 48"Givethepathtotheexamples/facesdirectoryastheargumenttothis" 49"program.Forexample,ifyouareinthepython_examplesfolderthen" 50"executethisprogrambyrunning: " 51"./train_shape_predictor.py../examples/faces") 52exit() 53faces_folder=sys.argv[1] 54 55options=dlib.shape_predictor_training_options() 56#Nowmaketheobjectresponsiblefortrainingthemodel. 57#Thisalgorithmhasabunchofparametersyoucanmesswith.The 58#documentationfortheshape_predictor_trainerexplainsallofthem. 59#YoushouldalsoreadKazemi'spaperwhichexplainsalltheparameters 60#ingreatdetail.However,hereI'mjustsettingthreeofthem 61#differentlythantheirdefaultvalues.I'mdoingthisbecausewe 62#haveaverysmalldataset.Inparticular,settingtheoversampling 63#toahighamount(300)effectivelybooststhetrainingsetsize,so 64#thathelpsthisexample. 65options.oversampling_amount=300 66#I'malsoreducingthecapacityofthemodelbyexplicitlyincreasing 67#theregularization(makingnusmaller)andbyusingtreeswith 68#smallerdepths. 69options.nu=0.05 70options.tree_depth=2 71options.be_verbose=True 72 73#dlib.train_shape_predictor()doestheactualtraining.Itwillsavethe 74#finalpredictortopredictor.dat.TheinputisanXMLfilethatliststhe 75#imagesinthetrainingdatasetandalsocontainsthepositionsoftheface 76#parts. 77training_xml_path=os.path.join(faces_folder,"training_with_face_landmarks.xml") 78dlib.train_shape_predictor(training_xml_path,"predictor.dat",options) 79 80#Nowthatwehaveamodelwecantestit.dlib.test_shape_predictor() 81#measurestheaveragedistancebetweenafacelandmarkoutputbythe 82#shape_predictorandwhereitshouldbeaccordingtothetruthdata. 83print(" Trainingaccuracy:{}".format( 84dlib.test_shape_predictor(training_xml_path,"predictor.dat"))) 85#Therealtestistoseehowwellitdoesondataitwasn'ttrainedon.We 86#traineditonaverysmalldatasetsotheaccuracyisnotextremelyhigh,but 87#it'sstilldoingquitegood.Moreover,ifyoutrainitononeofthelarge 88#facelandmarkingdatasetsyouwillobtainstate-of-the-artresults,asshown 89#intheKazemipaper. 90testing_xml_path=os.path.join(faces_folder,"testing_with_face_landmarks.xml") 91print("Testingaccuracy:{}".format( 92dlib.test_shape_predictor(testing_xml_path,"predictor.dat"))) 93 94#Nowlet'suseitasyouwouldinanormalapplication.Firstwewillloadit 95#fromdisk.Wealsoneedtoloadafacedetectortoprovidetheinitial 96#estimateofthefaciallocation. 97predictor=dlib.shape_predictor("predictor.dat") 98detector=dlib.get_frontal_face_detector() 99100#Nowlet'srunthedetectorandshape_predictorovertheimagesinthefaces101#folderanddisplaytheresults.102print("Showingdetectionsandpredictionsontheimagesinthefacesfolder...")103win=dlib.image_window()104forfinglob.glob(os.path.join(faces_folder,"*.jpg")):105print("Processingfile:{}".format(f))106img=dlib.load_rgb_image(f)107108win.clear_overlay()109win.set_image(img)110111#Askthedetectortofindtheboundingboxesofeachface.The1inthe112#secondargumentindicatesthatweshouldupsampletheimage1time.This113#willmakeeverythingbiggerandallowustodetectmorefaces.114dets=detector(img,1)115print("Numberoffacesdetected:{}".format(len(dets)))116fork,dinenumerate(dets):117print("Detection{}:Left:{}Top:{}Right:{}Bottom:{}".format(118k,d.left(),d.top(),d.right(),d.bottom()))119#Getthelandmarks/partsforthefaceinboxd.120shape=predictor(img,d)121print("Part0:{},Part1:{}...".format(shape.part(0),122shape.part(1)))123#Drawthefacelandmarksonthescreen.124win.add_overlay(shape)125126win.add_overlay(dets)127dlib.hit_enter_to_continue()

有需要的小伙伴們，快來試試這個模型吧！