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[course site]
Elisa Sayrol Clols
elisa.sayrol@upc.edu
Associate Professor
Universitat Politecnica de Catalunya
Technical U...
Face Recognition
•Face Detection
•Face Alignment/ Frontalization
•Face Recognition
•Face Identification (Classification)
•...
Face Detection and Recognition
Biometrics - Security is a relevant application of Face Recognition
[NEC’s NoeFace]
Databases
YouTube Faces: [http://www.cs.tau.ac.il/~wolf/ytfaces/]
621.126 pictures, 1.595 identities (celebrities).
Images...
Databases
Labeled Faces in the Wild (LFW)
[http://vis-www.cs.umass.edu/lfw/ ]
13.000 images of faces collected from the we...
DeepFace Architecture
Yaniv Taigman, etc (Facebook) . DeepFace: Closing the Gap to Human-Level Performance in Face Verific...
DeepFace, Verification
*S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity met-ric
discriminatively, with applicat...
FaceNet
Florian Schroff et al. (Google) FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR 2015
This F...
Deep ID2
Parameters of the Network:
But you compute parameters
From a Verification loss
function and an Identification
los...
Deep ID2
When you backprop you
backprop gradients of
verification and
identification parameters
and you also update the
we...
Deep ID2
DeepID2 Uses a Joint Bayesian model in top of the network for face
verification.
If we model a face as
Verificati...
Open Challenges in Face Recognition
Difficult initial detection:
Illumination, False positives, Gender
High intra-class va...
Questions?
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Face Recognition (D2L5 2017 UPC Deep Learning for Computer Vision)

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https://telecombcn-dl.github.io/2017-dlcv/

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.

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Face Recognition (D2L5 2017 UPC Deep Learning for Computer Vision)

  1. 1. [course site] Elisa Sayrol Clols elisa.sayrol@upc.edu Associate Professor Universitat Politecnica de Catalunya Technical University of Catalonia Face Recognition (with Ramon Morros) Day 2 Lecture 5 #DLUPC
  2. 2. Face Recognition •Face Detection •Face Alignment/ Frontalization •Face Recognition •Face Identification (Classification) •Face Verification(Binary Decision) •People Recognition in videos (Camomile Project at UPC) •Speech •Face •Text
  3. 3. Face Detection and Recognition Biometrics - Security is a relevant application of Face Recognition [NEC’s NoeFace]
  4. 4. Databases YouTube Faces: [http://www.cs.tau.ac.il/~wolf/ytfaces/] 621.126 pictures, 1.595 identities (celebrities). Images come from videos so there is not a lot of variability between them. May overlap with other celebrity databases Available info: Original frames, cropped faces, aligned faces. Head-pose angles for all the faces FaceScrub: [http://vintage.winklerbros.net/facescrub.html] 106.863 photos of 530 celebrities, 265 whom are male (55.306 images), and 265 female (51.557 images). Face bounding boxes provided. Full frame and cropped version available. MegaFace: [http://megaface.cs.washington.edu/] 1 milion faces, 690.572 unique people MSRA-CFW [http://research.microsoft.com/en-us/projects/msra-cfw/] 202.792 faces, 1.583 people (celebrities). May overlap with other celebrity databases.
  5. 5. Databases Labeled Faces in the Wild (LFW) [http://vis-www.cs.umass.edu/lfw/ ] 13.000 images of faces collected from the web, 1.680 of the people pictured have two or more Labeled Faces in the Wild: A Survey. In Advances in Face Detection and Facial Image Analysis, edited by Michal Kawulok, M. Emre Celebi, and Bogdan Smolka, Springer, pages 189-248, 2016. CelebFaces [http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html ] 202.599 face images of 10.177 identities (celebrities). People in LFW and CelebFaces+ are mutually exclusive. Other databases: https://deeplearning4j.org/opendata GoogleUPC !!!
  6. 6. DeepFace Architecture Yaniv Taigman, etc (Facebook) . DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR 2014
  7. 7. DeepFace, Verification *S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity met-ric discriminatively, with application to face verification, CVPR,2005. B) Use of Siamese Networks inspired in Chopra et al* A) Weighted χ2 distance where f1 and f2 are the DeepFace Representations. The weights parameters wi are learned using a linear SVM In DeepFace: αi are the trainable parameters with standard cross-entropy loss and backward propagation
  8. 8. FaceNet Florian Schroff et al. (Google) FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR 2015 This Face recognition/verification/clustering model learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Triplet Loss function: Where f is the embedding
  9. 9. Deep ID2 Parameters of the Network: But you compute parameters From a Verification loss function and an Identification loss Function Yi Sun, etc. Deep Learning Face Representation by Joint Identification-Verification. NIPS 2014
  10. 10. Deep ID2 When you backprop you backprop gradients of verification and identification parameters and you also update the weight of the convolutional layers
  11. 11. Deep ID2 DeepID2 Uses a Joint Bayesian model in top of the network for face verification. If we model a face as Verification is achieved through Log-Likelihood Ratio Test: Represents the Identity. Interpersonal variations Intrapersonal variations Both Gaussian Distributed, estimated during Training Chen, et al. Bayesian Face Revisited: A Joint Formulation, ECCV 2012 HI Interpersonal hypothesis HE Extrapersonal hypothesis
  12. 12. Open Challenges in Face Recognition Difficult initial detection: Illumination, False positives, Gender High intra-class variability: Apparels, Expression, Rotations, Aging Multimodality (especially in Video), Partial Annotations, Incremental Learning
  13. 13. Questions?

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