DeepFace: Face Generation using Deep Learning

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by  Hardie Cate

DeepFace: Face Generation using Deep Learning
HardieCate FahimDalvi ZeshanHussain
[email protected] [email protected] [email protected]
7
Abstract follows: Given a set of facial attributes as input,
1
0 produce an image of a well-formed face as output
2 WeuseCNNstobuildasystemthatbothclassi- thatcontainsthesecharacteristics.
n fiesimagesoffacesbasedonavarietyofdifferent In our face generation system, we fine-tune a
a facial attributes and generates new faces given a CNN pre-trained on faces to create a classifica-
J setofdesiredfacialcharacteristics. Afterintroduc- tionsystemforfacialcharacteristics. Weemploya

7 ing the problem and providing context in the first noveltechniquethatmodelsdistributionsoffeature
section, we discuss recent work related to image activationswithintheCNNasacustomizedGaus-
]
V generationinSection2. InSection3, wedescribe sianmixturemodel.Wethenperformfeatureinver-
themethodsusedtofine-tuneourCNNandgener- sion using the relevant features identified by this
C
atenewimagesusinganovelapproachinspiredby model. The primary advantage of our implemen-
.
s aGaussianmixturemodel.InSection4,wediscuss tation is that it does not require any deep learning
c
our working dataset and describe our preprocess- architecturesapartfromaCNNwhereasothergen-
[
ingstepsandhandlingoffacialattributes. Finally, erativeapproachesdo. Ourfacegenerationsystem
1
inSections5,6and7,weexplainourexperiments hasmanypotentialuses,includingidentifyingsus-
v
and results and conclude in the following section. pectsinlawenforcementsettingsaswellasinother
6
7 Our classification system has 82% test accuracy. moregenericgenerativesettings.
8 Furthermore, our generation pipeline successfully
1 createswell-formedfaces. 2.RelatedWork
0
.
1 Work surrounding generative models for deep
0 1.Introduction learning has mostly been in developing graphi-
7 cal models, autoencoder frameworks, and more
1
Convolutional neural networks (CNNs) are recently, generative recurrent neural networks
:
v powerful tools for image classification and object (RNNs). Specificgraphicalmodelsthathavebeen
Xi detection, but they can also be used to generate used to learn generative models of data are Re-
images. There are myriad image generation tasks stricted Boltzmann Machines (RBMs), an undi-
r
a andapplications,rangingfromgeneratingpotential rected graphical model with connected stochastic
mass lesions in radiology scans to creating land- visible and stochastic hidden units, and their gen-
scapesorartisticscenes. Inourproject,weaddress eralizations, such as Gaussian RBMs. Srivastava
the problem of generating faces based on certain and Salakhutdinov use these basic RBMs to cre-
desiredfacialcharacteristics. Potentialfacialchar- ate a Deep Boltzmann Machine (DBM), a multi-
acteristicsfallwithinthegeneralcategoriesofraw modalmodelthatlearnsaprobabilitydensityover
attributes(e.g.,bignose, brownhair,etc.),ethnic- the space of multimodal inputs and can be effec-
ity(e.g.,white,black,Indian),andaccessories(e.g. tivelyusedforinformationretrievalandclassifica-
sunglasses,hat,etc.). Theproblemcanbestatedas tion tasks [14]. Similar work done by Salakhut-
1
dinovandHintonshowshowthelearningofahigh neously trained: a generative model G that cap-
capacityDBMwithmultiplehiddenlayersandmil- tures the distribution of the data and a discrimi-
lionsofparameterscanbemademoreefficientwith native model D that estimates the probability that
a layer-by-layer ”pre-training” phase that allows a sample came from the training data rather than
for more reasonable weight initializations by in- G. GistrainedtomaximizetheprobabilitythatD
corporating a bottom-up pass [13]. In his thesis, makesamistake.
Salakhutdinovalsoaddstothislearningalgorithm
3.Methods
byincorporatingatop-downfeedbackpassaswell
as a bottom-up pass, which allows DBMs to bet-
Ourworkflowconsistsoftwosteps. Firstly,we
ter propagate uncertainty about ambiguous inputs
finetune a pre-trained model to classify facial and
[12].
imagecharacteristicsforaninputimage. Secondly,
Other generative approaches involve using au-
wegeneratefacesgivensomedescription. Wede-
toencoders.Thefirstideasregardingtheprobabilis-
scribeourapproachforeachstepintheforthcom-
ticinterpretationofautoencoderswereproposedby
ingsections.
Ranzato et al.; a more formal interpretation was
givenbyVincent,whodescribeddenoisingautoen- 3.1.Notation
coders (DAEs) [10] [15]. A DAE takes an input
To facilitate the discussion of our methods, we
x ∈ [0,1]d and first maps it, with an encoder,
introduce some notation. Let D denote our set of
to a hidden representation y ∈ [0,1]d(cid:48) through
trainingimages, andletF denotethesetof73fa-
some mapping, y = s(Wx + b), where s is a
cialattributes. Fromthispointforward,weusethe
non-linearity such as a sigmoid. The latent rep-
wordattributetoreferstrictlytooneofthe73facial
resentation y is then mapped back via a decoder
characteristicsinoursystem.
intoareconstructionzofthesameshapeasx,i.e.
z=d(W(cid:48)y+b(cid:48)). Theparameters,W,b,b(cid:48),and 3.2.Fine-tuning
W(cid:48)arelearnedsuchthattheaveragereconstruction
For finetuning, we employ the VGG-Face net,
loss between x and z is minimized [9]. Bengio et
a 16-layer CNN that was trained on 2 million
al. showanalternateformoftheDAE:givensome
celebrityfacesandevaluatedonfacesfromtheLa-
observedinputX andcorruptedinputX(cid:101),whereX(cid:101)
beledFacesintheWildandYouTubefacesdatasets
has been corrupted based on a conditional distri-
[11].UsingtheVGG-Facenetasourbasearchitec-
butionC(X(cid:101)|X), wetraintheDAEtoestimatethe
ture,wethenattach42headstotheendofthefc-7
reverseconditionalP(X|X(cid:101))[2]. Withthisformu- layeroftheVGG-Facenet,eachofwhichconsists
lation,Vincentetal. constructadeepernetworkof
ofafullyconnectedfc-8layerandasoftmaxlayer.
stackedDAEstolearnusefulrepresentationsofthe
Each softmax head is a multilabel classifier for a
inputs[16].
group of attributes that are highly correlated. For
AnalternatemodelhasbeenpositedbyGregor example,onegroupofattributesincludeshaircolor
etal.,whoproposeusingarecurrentneuralnetwork (black,gray,brown,andblond);ingeneral,some-
architecturetogeneratedigits. Thisarchitectureis oneusuallyonlyhasonecolorofhair,whichmakes
atypeofvariationalautoencoder,arecentadvanced groupingtheseparticularattributestogetherasone
modelthatbridgesdeeplearningandvariationalin- multilabel classification reasonable. Furthermore,
ference, since it is comprised of an encoder RNN grouping together highly correlated attributes for
thatcompressestherealimagesduringtrainingand our classification task in lieu of having 73 binary
a decoder RNN that reconstitutes images after re- classifiers provides the network with implicit in-
ceivingcodes[5]. formationontherelationshipbetweengroupedfea-
Finally, another approach for estimating gener- tures.
ative models is via generative adversarial nets [3] During training, we freeze certain layers in the
[4]. In this framework, two models are simulta- CNN and learn the weights and biases on the un-
2
Figure1: ModifiedVGG-FaceArchitecture
frozenlayers. Todeterminewhichlayerstofreeze, This baseline serves primarily to demonstrate that
we run several experiments with different sets of our CNN correctly represents important facial
frozen layers and choose the set with the optimal structures such as eyes, noses, mouths, etc., even
performance. We describe the experiments to as- after fine-tuning on specific facial attributes. This
sess our architecture and the results of the fine- approach is not comprehensive enough because
tuninginSection5. boosting certain features from the last layer in the
network does not limit the number of facial struc-
3.3.Generation
tures that appear in the image. A more complete
discussion of the results of the baseline approach
3.3.1 BaselineApproach
canbefoundintheSection5,6.
We begin the generation phaseof our project with Our next and final approach uses a customized
a simple baseline approach that uses class visual- variant of a Gaussian Mixture Model (GMM). We
ization. This approach begins with the mean im- optforthistechniqueovermoretraditionalgenera-
age M, which is the pixel-wise and channel-wise tiveapproachesdiscussedinSection2fortwomain
meanofallimagesinourtrainingset.Weaddsmall reasons. Firstly, to our knowledge, this method is
GaussiannoisetoM andusethisimageX asour novel and has not been used in context of image
input to our baseline algorithm. Suppose we wish generation using CNNs. Secondly, this approach
toproduceafacewithasetofattributesC ⊆F. In does not require complex structures in addition to
each iteration of our algorithm, we do the follow- ourexistingCNN,sothemodelissimplerandhas
ing: farfewerparameterstotrain.
1. PerformaforwardpasswithX asinput;
3.3.2 CustomGaussianMixtureModel
2. Set the gradients at each softmax layer to ei-
This approach behaves similarly to the baseline in
ther1or0,dependingonourtargetattributes
thatwebeginwithaninputimageX thatconsists
C;
ofrandomnoiseandboostthisimagetowardaset
3. Performabackwardpassthroughthenetwork of attributes C ⊆ F. The primary difference is
withthesegradients; that instead of class visualization, we use feature
inversion with respect to a layer in the CNN. We
4. Update X using stochastic gradient descent also begin with random noise instead of the mean
(SGD)andregularization. image.
3
Intuitively, feature inversion tries to minimize Specifically,foreachattributef ,f ,...,f ∈
1 2 73
the difference in the activations of an input image F, we select 73 random sets of images
andsometargetactivationsinacertainlayerofthe S ,S ,...,S ⊆ D of equal size such that
1 2 73
CNN. These target activations are the activations for each i, every image in S has attribute f .
i i
thatwebelieveaninputimagewiththedesiredat- Thenforeachi,wecomputeameanvectorµ and
i
tributesCwillhavewhenpassedthroughtheCNN. covariance matrix Σ based on the activations at
i
More formally, Let φ (I) be the activations at layer l of the images in S . These mean vectors
l i
layerlwhenanimageIispassedthroughtheCNN. and covariance matrices define 73 independent
Let T denote the target activations for layer l and multivariateGaussiandistributions.
l
supposewehaveawayofdeterminingT fromC. Thus,accordingtoourmodel,ifwewishtopro-
l
We wish to find an image I∗ by solving the opti- duce possible activations at l of an image that has
mizationproblem attribute f and no other f ∈ F, we can simply
i
sample from N(µ ,Σ ). More generally, we can
I∗ =argmin(cid:107)T −φ (I)(cid:107)2+R(I) (1) i i
l l 2 estimatethetargetactivationsforasetofattributes
I
byperformingaweightedsumofsamplesfromthe
where(cid:107)·(cid:107)2 isthesquaredEuclideannormandR
2 Gaussians,whichweassumetobeindependent. If
isaregularizersuchasblurringand/orjitter. Given
wewishtoestimatethetargetactivationsatlfora
T andX,featureinversiondoesthefollowing:
l subset C ⊆ F of attributes, we would produce a
1. PerformaforwardpasswithX asinputupto samplesgivenby
layerl;
73
1 (cid:88)
2. Set the gradients at l by taking the gradient s= |C| 1iwizi (2)
oftheobjectivefunctionabovewithrespectto i=1
theactivationsinlayerl; where 1 = 1 if f ∈ C and 0 otherwise, w is
i i i
3. Perform a backward pass from l with these the weight assigned to the ith Gaussian, and zi ∼
N(µ ,Σ )isarandomvariable. Thisscanbeused
gradients; i i
inplaceofT forfeatureinversion.
l
4. UpdateX usingSGDandregularization. Nowwemustdeterminetheweightsvectorw =
(w ,...,w ). In our approach, we learn this
The challenge of this approach is determining 1 73
weights vector by trying to find weights that min-
anappropriatesetoftargetactivationsT givenC.
l
imizethedifferencebetweenthetargetactivations
To our knowledge, there is no established way to
produced by the sampling method above and the
automatically detect these target activations given
actualactivationsforimages.Wewishtofindthese
solelythedesiredfacialattributes.
weightsbysolvingtheoptimizationproblem
To address this problem, we introduce a cus-
tom Gaussian Mixture Model (cGMM), in which
|D|
wemodelthedistributionofTlforagivenattribute w∗ =argmin 1 (cid:88)(cid:107)φ (I )−Φ(cid:107)2+λ(cid:107)w(cid:107)2
f asamultivariateGaussiandistribution. Foreach w |D| l i 2 2
i=1
f ∈ F, we estimate the mean and covariance ma- (3)
trixofitsGaussianbysamplingimagesinourtrain- where
ingsetthatarepositiveexamplesforthisattribute. 1 (cid:88)73
Φ= 1 w µ (4)
For computational simplicity, we assume the co- |C | i,j j j
i
variance matrix for each distribution is diagonal. j=1
This assumption implies that we effectively treat and1 indicateswhetherf ∈C ,theattribute
i,j j i
each multivariate Gaussian as a stacking of many set for image i. We learn the weights by taking
independent Gaussians that model the distribution derivatives of this objective function with respect
ofactivationsforasingleneuroninl. towanditerativelyupdatingwusingSGD.
4
4.DatasetsandFeatures
(a)Youth (b)Soft-lighting
Figure2: Variationsinlightingconditionsand
cameraangles
Figure3: 2Drepresentationoffc-7featurespace
We are currently using the PubFig dataset [8],
whichisacollectionoffacesfrompublicphotosof
200 individuals. The dataset includes 58K images white, while only 3% are black. The number of
scraped from various sources on the Internet, and peopleofotherethnicitiesisevenlower,soalarge
bounding boxes around the face for each of these portionoftheimagesdonothaveanyethnicityla-
images.However,toavoidcopyrightissues,theau- beling. Anotherprimeexampleofsuchadisparity
thorsprovidelinkstotheimages,insteadofhosting iseyewear.Around90%ofthepeopleinthedataset
the images themselves. We have roughly 20,000 havenoformofeyewearinthephotos. Asanother
imagesinourtrainingsetand8,000imagesinour exampleoftheincompletenessofourdataset,only
testset. Themissingimagesincludethosethatdo 60%ofthepeoplehaveageinformationassociated
notexistatthegivenlinks,thosethathavechanged withtheirfaces.Thesenumbersgiveusagoodidea
andafewthathadincorrectboundingboxesforthe ofthelimitsofthesystemwearebuildingandhow
faces. certain attributes might generate better faces than
Sincetheseimagesarescrapedfrompublicpho- others.
tographs,thereareseveralvariationsinfaces.Most AsmentionedinSection3.2, weusetheVGG-
faces are frontal, while some are at slight angles. Face net as our base for finetuning. Before train-
Thereisalsoawidespectrumoflightingconditions ingthenetwork,weperformsomeanalysistosup-
inthedataset. port the validity of VGG-Face net as our base
Foreachimageinthedataset,wealsohavealist architecture. Specifically, we perform a forward
of 73 attributes including gender, eye color, face pass for a set of images from our dataset and ob-
shape and ethnicity. Many of these attributes are tain activations at the fc-7 layer. We then trans-
interdependent. For example, only one of the at- formthespaceoftheseactivationsintotwodimen-
tributes within black hair, blond hair and brown sions using t-SNE. As evidenced by Figure 3(a),
hairispositivelylabeledforeachindividual. weseeclusteringevenwhenusingtheweightsdi-
rectly from VGG-Face net. This gives us con-
5.Experiments&Analysis fidence that the network is a good candidate for
transfer learning. However, clustering does not
5.1.FinetuningExperiments
occur for all of the attributes, as seen in Figure
Tounderstandthedatabetter,wefirstrunsome 3(b). This is also expected, since the VGG-Face
experimentstofindoutwhatthedistributionofour netlearnedattributesthatwereimportantindistin-
data looks like. For each image, we are given in- guishing celebrity faces from each other, and cer-
formation about the presence or absence of 73 at- tainattributeslikesoft-lightingmaynotprovidethe
tributes. In Figure 7 we see that the distribution discriminatorypowertodoso. Afterlookingatthe
is quite skewed towards certain attributes. For ex- overallresultsacrosstheattributes,wedecidedthat
ample,around76%ofthepeopleinthedatasetare weneedtobackpropagateourgradientsintoatleast
5
haslearnedwhatthesegeneralstructuresintheim-
ages look like. Because class visualization seems
Figure5: TargetImage,VanillaFeatureInversion,
cGMMFeatureInversion
toboostanypartoftheimagewhoseactivationsare
closetothedesiredfeatures,weshiftourapproach
to feature inversion. For some image with desired
Figure4: Losscurveafterunfreezingvarious
attributes, there exist corresponding activations at
layersinthenetwork
an arbitrary layer that we can use to generate the
image.Tosanitycheckthisidea,weperformanex-
someofthelayersinthenetwork. perimentwhereweusethegroundtruthactivations
Next, we tune hyperparameters to get the best ataconvolutionallayerforaBarackObamaimage
resultsfromtransferlearning. Wetryawiderange andattempttoreconstructObamausingtheseacti-
of learning rates, diagnosing the learning process vations. Weobtaintheseactivationsbyperforming
by looking at the loss curve. We also try various a forward pass on the image and stopping at the
configurations of freezing layers in the network. desired convolutional layer. The resultant Barack
AswecanseefromFigure4,backpropagatinginto Obama image is shown in Figure 5. This result
morelayersinthenetworkhelpsthelearningpro- shows that the feature inversion method is a rea-
cess,butthefinallossisverysimilaracrossalltri- sonableapproachaslongaswehaveaccesstothe
als. Wealsotrydifferentlearningratesforthelay- ”target” activations of an image with selected at-
ers based on their distance to the heads. Results tributes. As described in our methods, we use a
weresimilartotheprevioustrials,withthenetwork cGMMtoestimatethesetargetactivations.
convergingtomoreorlessthesameloss.
5.2.ImageGenerationExperiments
Wefirstrunseveralexperimentsusingourbase-
line class visualization approach. For our pri-
mary experiment, we attempt to boostseveral fea-
tures that vary in frequency of appearance in our
dataset,includingblack (ethnicity)andblackhair, (a)Distributionofelement (b)Distributionofelement
forwhichtherearefewerimages,andmiddle-aged, infc-6activationsforblack infc-6activationsformale
smiling, and male, for which the frequency of im- attribute attribute
agesismuchhigher. Aresultantimageofthisex-
periment is shown in Figure 5. We see that the fi-
nal image has multiple facial structures, i.e. mul- Figure6: 2Drepresentationoffc-6featurespace
tiple sets of noses, eyes, eyebrows, etc. However,
the image also contains some semblance of black To justify modeling each attribute by an inde-
hair and a facial structure that is more masculine. pendentmultivariateGaussian,wetaketheimages
Our baseline demonstrates that the trained CNN insetS forlabelf andforeachimage,plotasin-
i i
6
gle element of the activation at the fc-6 layer. We person similarity%
thenplotthedistributionofthatparticularelement BarackObama 35
intheactivationacrossalltheimagesforthatlabel CliveOwen 27
i. Lookingattwodistributionsrepresentingtwoel- CristianoRonaldo 45
ements of the fc-6 activation in Figure 6, we see JaredLeto 52
thatthedistributionsarereasonablyclosetoanor- JuliaRoberts 42
maldistribution. Ingeneral,afterplottingdistribu- MickeyRourke 22
tions for each element across a sample of images MileyCyrus 42
for an arbitrary label, any arbitrary distribution of NicoleRichie 30
element j of the fc-6 activation for label i seems RyanSeacrest 30
tobenormal(notallgraphsareshown). Thus,be-
causewehavesomeconfidencethatthetruedistri- Table2: SimilarityPercentagesforDifferentFaces
butionofthetargetactivationsisGaussian,wecan
proceedwithourcGMMformulation.
that are defined by several images in the test set
Now, we perform experiments to learn the
(i.e. wepluginthesetargetattributesintooursys-
weightsinourcGMM.Theforwardandbackward
tem and compare the image we generate with the
passes of are written from scratch, so we use nu-
ground truth image). One example of a generated
merical gradient checking to validate the analytic
imageisinFigure8. Tomeasurehow”reasonable”
gradients. To choose the learning rate and regu-
our faces are, we use a similarity metric imple-
larizationparameters,westartbyarbitrarilysetting
mentedbythePicTrievsoftware,whichusescom-
thelearningrateandregularizationparameterto1e-
monfacialfeaturestomeasuresimilarity[1]. This
6 and 1e-5, respectively. Then, we incrementally
quantitativeevaluationofourfacesisshowninTa-
reduce the learning rate until the loss starts to de-
ble2.
crease instead of diverging to infinity. Finally, we
use the trained model to generate several faces of
differingattributes.
6.Results
trainingset testset Figure8: CristianoRonaldogeneratedimage
fc-6 0.847 0.795
conv-5 0.867 0.815
7.Discussion
Table1: Averageclassificationaccuracyfreezing
parametersbeneathlayersfc-6andconv-5
Although our classification accuracies are gen-
erally quite high, there are some facial attributes
Our final training parameters for the finetuning suchaslighting(e.g.,softlighting,harshlighting)
are5e-6forthelearningrateand50forthenumber andphototype(e.g.,colorphoto,posedphoto)that
of epochs. As a form of regularization, we utilize havelowaccuracies(roughly55-60%). Wedonot
dropout after every layer. The average classifica- attempt to improve these attributes, since they are
tion accuracies are shown in Table 1. The classi- notasimportantasother,moreface-relatedcharac-
ficationaccuracyforeveryfacialattributeisabove teristics,suchasnoseshapeandforeheadvisibility,
0.5,whilemostarearound0.8or0.9. thathavehighclassificationaccuracies.
For our cGMM training, our final learning rate Themostobviousresultfromourgenerationex-
and regularization parameter are 1e-11 and 1e-5, perimentsisthateachresultantimageisveryclose
respectively.Wegeneratefacesforsetsofattributes tothemeanimageofourtrainingset. Despitethis,
7
Figure7: Distributionofallattributesinourtrainingdataset
the fact that we successfully generate a coherent sired image by taking a weighted sum of samples
face from random noise indicates that our feature from the Gaussians instead of a weighted sum of
inversion process could work if the activations for themeansoftheGaussians. Weoptedforthelatter
eachattributeweremorediscriminative. approachbecauseitwasfarmorecomputationally
Wehypothesizeseveralreasonswhyouractiva- efficient, but the former approach could allow for
tionsarenotdiscriminativeenough. Onepossibil- morevariationfromthemeanimage.
ity is that the distributions for different attributes
8.Conclusion&FutureWork
areverysimilarandclusteredaroundthemeanim-
age in this abstract space. Another possibility is
In this work, we have trained an architecture
that the distributions lie in different manifolds in
that classifies facial attributes of images with high
thisspace,butthattheyarenotnormalyetroughly
accuracy. We have also shown the effectiveness
centeredaroundthemean-image. Thismeansthat
of the custom Gaussian Mixture Model in gener-
theactofaveragingtheseactivationsandapproxi-
ating well-formed faces from random noise. Al-
matingthemasGaussianblursthedistinctionsbe-
though the faces themselves were not discrimina-
tweenthesedistributions.
tive enough for the desired features, we posit that
This bring us to the assumptions about the fea- wecantrainamodelthatgeneratesdiscriminative
ture activations upon which the cGMM approach facesifourdatasetcontainedmorediversityacross
relies. Our first assumption is that the distribution theattributes.
of activations for each attribute in a given layer Alternatively,wemightimplementavariational
is indeed Gaussian. Additionally, we assume that autoencoderstructureasin[5]andcomparethere-
these Gaussians are independent. This is perhaps sultswiththoseofthecGMM.Weexpectthatsince
theleastjustifiedassumptionthemodelmakes,be- the variational autoencoder has a higher capacity,
cause clearly many attributes are correlated, such it might give more discriminative faces but would
as middle-aged and white hair. Despite this, we alsotakelongertotrain. Moreefficientgenerative
hopethatourmodelapproximatesthetruedistribu- modelsopenupexcitingpossibilitiesinotherappli-
tionsufficientlyforourpurposes. Wealsoassume cations,includingmedicineandlawenforcement.
that the variables in the multivariate Gaussian are
themselves distributed normally and are indepen-
dent. We believe this assumption is valid because
weexaminedseveralofthesedistributionsforindi-
vidualvariablesasdescribedinSection5.
One way to improve our cGMM technique
wouldbetoestimatethetargetactivationsofade-
8
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