Project Proposal: Robust Face Detection for the facial expression recognition

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Project ProposalRobust Face Detection for the
facial expression recognition

• Jin Hyun, Kim
• jinhyun_at_paradise.kaist.ac.kr

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Purposes

• To make the system, robust face detection module,
  for the facial expression recognition
• What is the robust face detection?
• Tolerance of the Background complexity
• Robustness of the Color variance
• Brightness Contrast invariance
• Real-time Face detection

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Specification (1)

• Assumption/Constraint
• Inside of the building (ex. The room of the
  laboratory)
• On-line Image sequence
• Goal
• 80 success rate (10 persons)

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Specification (2)

• Environment of Implementation
• USB camera
• ??35?, ???640?480, frame rate 30 frames/sec
• Common Personal Computer
• Intel 2.4G Hz CPU, OS Windows XP
• Language C
• Development tool Visual C 6.0

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Future Works

• Algorithm issue
• What is the most adaptive algorithm for the
  robust face detection in complex environment?
• Implementation issue
• How can improve the speed of the system?
• Further consideration
• ???? ????? ?? ?? ??? ?? ??? ??? ??? ????

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Face Detection A Survey

• Erik Hjelmas, Boon Kee Low
• Computer Vision and Image Understanding 83,
  pp236-274 (2001)

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Contents

• Introduction
• Evolution of Face Detection Research
• Feature-Based Approach (?? ?? ??)
• Low-Level Analysis
• Feature Analysis
• Active Shape Models
• Image-Based Approach (??? ?? ??)
• Linear Subspace Methods
• Neural Networks
• Statistical Approaches
• Applications
• Summary Conclusions

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Feature-Based Approach

• Low-Level Analysis
• Edges, Gray Information, Color, Motion,
  Generalized Measures
• Feature Analysis
• Feature Searching
• Constellation Analysis
• Active Shape Models
• Snakes
• Deformable Template
• Point Distributed Models

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Feature Analysis

• Low-level analysis ambiguous
• ???? ???? ??? ?? ?? ??
• Classical many to one mapping problem
• ??? ???? ???(characterization)?? ??(verification)

• Feature Searching
• ??? ?? ???? ???? ??? ????.
• ??? ??? ??
• Constellation Analysis
• ??? ?? ??? ???? ??? ?? (flexible
  constellations)

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Feature Searching (1)

• Determination of prominent facial features
• A pair of eyes commonly applied reference
  feature
• Facial feature extraction algorithm (De Silva)
• ?????(the top of head)? ?? ? ??(eye-plane)? ??
  ???
• ??? ?? ??, ???? ??? ?? ?? ?? ??? ??.

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Feature Searching (2)

• A system for face and facial feature detection
  based on anthropometric measures (Jeng et. al.)
• ??? ?? ?? ?? ?, ? ??? ?? ??? ??. ? ?? ??? ??
  ????? ???.
• GAZE
• Automatic facial feature searching algorithm
• Based on the motivation of eye movement
  strategies in the human visual system (HVS)

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Constellation Analysis

• Feature Searching
• heuristic information fixed condition
• Grouping facial features in Face-like
  constellation
• Use of statistical shape theory
• To handle missing feature and problems due to
  translation, rotation, and scale to a certain
  extent.
• ???? ?? ?? ??? ?? ???

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Active Shape Models

• Actual physical and hence higher-level appearance
  of features.
• Three types of the active shape model
• Snakes generic active contour
• Deformable templates ?? ??? ???(priori)? ??,
  snake? ??
• PDM (Point distributed model)

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Snakes (Active Contour)

• Commonly used to locate a head boundary
• Two main considerations in implementing a snake
• The selection of the appropriate energy terms
• The energy minimization technique
• Two problems
• Part of the contour often becomes trapped onto
  false image features.
• Snakes are not efficient in extracting nonconvex
  features due to their tendency to attain minimum
  curvature.

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Deformable Template

• The concept of snakes the global information of
  the eye
• A deformable eye template based on its salient
  features is parameterized using 11 parameters.
• Working according to the same principle as a
  snake, the template once initialized near an eye
  feature will deform itself toward optimal feature
  boundaries.
• Several major considerations
• The evolution of a deformable template is
  sensitive to its initial position
• The processing time is also very high due to the
  sequential implementation of the minimization
  process.
• The weights of the energy terms are heuristic and
  difficult to generalize.

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Point Distributed Model

• PDM is a compact parameterized description of the
  shape based upon statistics.
• The advantages of using a face PDM
• A compact parameterized description
• The global characteristic of the model also
  allows all the features to be located
  simultaneously and thereby removes the need for
  feature searching.
• The occlusion of a particular feature does not
  pose a severe problem since other features in the
  model can still contribute to a global optimal
  solution

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Image-Based Approach

• The unpredictability of face appearance and
  environmental conditions.
• More hostile scenarios detecting multiple faces
  with clutter-intensive backgrounds.
• Pattern recognition problem

• Linear Subspace Methods
• Neural Networks
• Statistical Approaches

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Linear Subspace Methods

• Multivariate statistical analysis
• PCA (Principle Component Analysis)
• LDA (Linear discriminant Analysis)
• FA (Factor Analysis)

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PCA (Principle Component Analysis)

• Algorithm
• Given an ensemble of different face images, the
  technique first finds the principal components of
  the distribution of faces, expressed in terms of
  eigenvectors (of the covariance matrix of the
  distribution).
• Each individual face in the face set can then be
  approximated by a linear combination of the
  largest eigenvectors, more commonly referred to
  as eigenfaces, using appropriate weights.

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Statistical Approaches

• Information Theory
• Kullback relative information (Kullback
  divergence) maximum likelihood face detection
• SVM (Support Vector Machine)
• Bayes Decision Rule

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Comparative Evaluation

• How does one count correct detection and false
  positives?
• What is the systems ROC curve?
• What is the size of the training set and how is
  training implemented?
• What is the face?

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Applications

• Biometric identification
• Video conferencing
• Indexing of image and video database
• Intelligent human-computer interface

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Discussion

• What is the face?
• How can we automatically count correct detection
  and false positives?
• How can we define the normalized face image in
  the source image?