Mid term presentation

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Mixture of SVMs for Face Class Modeling
J.Meynet, V.Popovici, J.-Ph. Thiran
MLMI 04
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Outline

• Introduction
• Presentation of the Work
• Context
• The face detection task
• Principal Component Analysis
• Classification with Support Vector Machines
• Mixture of Support Vector Machines
• With independent subsets
• With k-means clustering
• Experiments and Results
• Conclusions and Future Work

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Face Detection Methods

• Image-Based Detection
• Consider face as a whole object
• Eigenfaces
• Fishers Linear Discriminant
• Neural Network, SVM
• HMM
• SNoW
• Geometrical-based methods
• Find precise parts of the face
• and reassemble them for the final decision
• Top-down
• Bottom-up

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Principle of the detection

• Pre-processing with a cascade of boosted
  Haar-Like Features
• gtReal-time face detector
• Principal Component Analysis (PCA)
• Dimensionality reduction
• Classification with a Mixture of SVM
• Random sampling or k-means clustering

• P.Viola, M.Jones, Robust real-time object
  detection. International Journal of computer
  Vision, 2002.

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Eigenfaces Space

• PCA and Eigenfaces

• Sirovich, Kirby, Low-dimensional procedure for
  the characterization of human faces, 1987

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Distance From Feature Space

• DFFS
• Construction of the classification vector

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Support Vector Machines (SVM)

• Find the hyperplane that correctly separates the
  data while maximising the margin.
• Optimisation
• Lagrange multipliers ?i
• Kernels

• V. Popovici, J.-Ph. Thiran, "Face Detection using
  SVM Trained in Eigenfaces Space", 4th
  International Conference on Audio- and
  Video-Based Biometric Person Authentication,
  Surrey, UK, 2003

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Mixture of SVMs (MSVM)

• Why? Building a face detection system requires a
  large amount of examples gt make the training
  easier

• Principle
• 1- Split initial dataset into N1 subsets
• By random sampling
• Or by K-means clustering
• 2- Train N first SVMs
• 3- Pass the N1th subset through the SVMs, train
  the 2nd layer SVM on the margins.

X
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Mixture of SVMs

• 2 Sampling techniques
• 1- Random partitioning
• M1 independent subsets
• 2- Clustering
• - Draw 1 random subset for the SVM-L2
• - K-means clustering on the remaining examples
• M clusters for training the SVM-L1-i
• SVM-L1-i are trained using cross-validation, with
  RBF kernels
• SVM-L2 trained on the margins
• It learns a function that assembles the
  confidences of each individual expert.

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Mixture of SVMs

• Output of the mixture
• Advantages
• Single SVM problem of complexity
• MSVM problems of
  complexity
• gt clearly advantageous
  

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Experiments and Results I

• Database
• Face images from Banca and XM2VTS
• Non faces chosen by bootstrapping on randomly
  selected images
• Estimation of a correct dimensionality for the
  eigenfaces space

20x15 images
Number of eigenfaces needed to keep 85 of total
variation
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Experiments and Results II

• Random sampling or clustering

(x5)
SVM-L1-i
(x5)
1000 F
2000 NF
8256 F
14000 NF
SVM-L2
2256 F
4000 NF

• Random sampling
• Reduce the importance of outliers or unusual
  examples
• Clustering
• Each SVM-L1-i performs like an expert on its own
  domain

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Experiments and Results III

• Generalisation

• Better generalisation capabilities than a single
  SVM
• MSVM improves the training time and the true
  positive rate
• Less Support Vectors gt lower computation
  complexity.

• Results on Banca images pre-processed by
  boosted Haar-Like features

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Conclusions - Future Work

• Boosted local feature-based classifiers
  pre-pocessing
• real-time processing
• Dimensionality reduction by PCA ( DFFS)
• Decrease the complexity of the classification
  task
• Extension to the SVM technique which performs
  well on large datasets.
• Decrease the training and classification time
• Improve discrimination capabilities
• Try other clustering techniques in eigenfaces
  space based on more appropriate metrics.