Face Recognition through Deep Neural Network - UTKweb.eecs.utk.edu/~hqi/deeplearning/lecture08-cnn-vggface.pdf · Face Recognition through Deep Neural Network Yang Song. Face Recognition,

FaceRecognitionthroughDeepNeuralNetwork

YangSong

FaceRecognition,IdentificationandVerification

ImplementationofVGG16

Dataaugmentation

FaceID

FaceIdentification:

Trainingset

QueryImage

FaceIdentification FaceID:TaylorSwift

FaceVerification:

TrainingsetQueryImages

FaceVerification Whethertheyarethesameperson?

FaceRecognition=FaceIdentification+FaceVerification

A facerecognitionsystem isa computerapplication capableof identifying or verifying apersonfroma digitalimage ora videoframe froma video source.Oneofthewaystodothisisbycomparingselected facialfeatures fromtheimageandaface database.

TraditionalMethod

FaceDetectionFeatureExtractionGivenImage

e.g.,Physicalfeatures:therelativeposition,size,shapeofeyes,nose,jawandetcsSkincolor;SIFTorHOGfeatures

Classifier/Model output

FaceIdentification/Verification

e.g.,SVMorabayse modele.g.,Landmarkdetection

Limitations?Intherealapplication,therearelargevariationwithfacepose,background,illuminationandocclusion.Itishardtodesignafeatureextractionmethodtoberobustanddiscriminative.

Whyourhumanbraincanfigureitout?

FaceDetection FeatureExtraction&FaceIdentification/VerificationGivenImage

output

ACNNNetwork

VGGFaceNetwork

FeatureMapSize

W

W

K

K

W=224K=3n_input =3N_output =64

Padding Stride Size Depth

SAME 1

SAME 2

VALID 1

VALID 2

FeatureMapSize

W

W

K

K

W=224K=3n_input =3N_output =64

Padding Stride Size Depth

SAME 1 224*224 64

SAME 2 112*112 64

VALID 1 222*222 64

VALID 2 111*111 64

FeatureMapSize

W

W

K

K

W=224K=5n_input =3N_output =64

Padding Stride Size Depth

SAME 1 64

SAME 2 64

VALID 1 64

VALID 2 64

FeatureMapSize

W

W

K

K

W=224K=5n_input =3N_output =64

Padding Stride Size Depth

SAME 1 224*224 64

SAME 2 112*112 64

VALID 1 220*220 64

VALID 2 110*110 64

If”VALID”,W_out =ceil((W– K+1)/S)If“SAME”,W_out =ceil(W/S)

VGG16Tensorflow Implementationhttps://www.cs.toronto.edu/~frossard/vgg16/vgg16.py

VGG16Tensorflow Implementationhttps://www.cs.toronto.edu/~frossard/vgg16/vgg16.py

Another lightweight implementation of VGG16

TF-Slim is a lightweight library for defining, training and evaluating complex models in TensorFlow.

Another lightweight implementation of VGG16

TF-Slim is a lightweight library for defining, training and evaluating complex models in TensorFlow.

Or

VGG16 byTF-Slim

DataAugmentation

Avoidoverfitting!IncreaseDatasetTranslation-robusttotinyshiftRotation-robusttoposevariationCompression-berobusttopoorresolution

Translation Flipping

Rotation RandomCropping

Compression

tf.random_croptf.image.resize_image_with_crop_or_padtf.image.random_flip_left_right