Project Proposal Robust Face Detection for the facial expression by knowledgegod

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									          Project Proposal:
       Robust Face Detection
for the facial expression recognition

                  Jin Hyun, Kim

   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
Specification (1)

   Assumption/Constraint
    –   Inside of the building (ex. The room of the laboratory)
    –   On-line Image sequence

   Goal
    –   80% success rate (10 persons)
Specification (2)

   Environment of Implementation
    –   USB camera
            화소:35만, 해상도:640ⅹ480, frame rate: 30 frames/sec
    –   Common Personal Computer
            Intel 2.4G Hz CPU, OS: Windows XP
    –   Language: C++
    –   Development tool: Visual C++ 6.0
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
    –   효과적인 표정인식을 위해 어떤 정보가 얼굴 검출
        의 결과로 나와야 하는가?
Face Detection : A Survey

     Erik Hjelmas, Boon Kee Low
Computer Vision and Image Understanding 83,
             pp236-274 (2001)
   Introduction
   Evolution of Face Detection Research
   Feature-Based Approach (특징 기반 방법)
       Low-Level Analysis
       Feature Analysis
       Active Shape Models
   Image-Based Approach (이미지 기반 방법)
       Linear Subspace Methods
       Neural Networks
       Statistical Approaches
   Applications
   Summary & Conclusions
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
Feature Analysis
   Low-level analysis:          Feature Searching
    ambiguous                     : 개인의 얼굴 특징들의 상대
    배경색이 얼굴색과 비슷할 경               적인 위치에 기초한다.
    우 문제 발생                       : 경험적 정보에 의존

   Classical many to one        Constellation Analysis
    mapping problem               : 다양한 얼굴 모델을 사용하
    다양한 특징들을 특성화                  는 유연한 배열 (flexible
    (characterization)하고 입증       constellations)
Feature Searching (1)
   Determination of prominent facial features
       A pair of eyes: commonly applied reference feature

   Facial feature extraction algorithm (De Silva)
       머리꼭대기(the top of head)를 찾고 눈 영역(eye-
        plane)을 찾는 순서로…
       안경을 쓰고 있고, 머리칼이 이마를 덮을 경우
        검출 실패가 있다.
Feature Searching (2)
   A system for face and facial feature detection:
    based on anthropometric measures (Jeng et. al.)
    : 후보로 뽑힌 눈에 대해 코, 입 그리고 눈썹 탐색을 한
    다. 각 얼굴 특징은 관련 평가함수를 가진다.

   GAZE
       Automatic facial feature searching algorithm
       Based on the motivation of eye movement
        strategies in the human visual system (HVS)
Constellation Analysis
   Feature Searching
       heuristic information: fixed condition
   Grouping facial features in Face-like
       Use of statistical shape theory
       To handle missing feature and problems due to
        translation, rotation, and scale to a certain extent.
       대부분의 검출 실패: 현저한 머리 움직임
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: 안면 특징의 선험성(priori)
        를 고려, snake의 향상
       PDM (Point distributed model)
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
     Snakes are not efficient in extracting nonconvex features due
      to their tendency to attain minimum curvature.
Deformable Template
   The concept of snakes + the global information of the
       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
   Several major considerations
       The evolution of a deformable template is sensitive to its initial
       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
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
Image-Based Approach
   The unpredictability of       Linear Subspace
    face appearance and            Methods
    environmental                 Neural Networks
    conditions.                   Statistical Approaches
   More hostile scenarios:
    detecting multiple faces
    with clutter-intensive
   Pattern recognition
Linear Subspace Methods
   Multivariate statistical analysis
       PCA (Principle Component Analysis)
       LDA (Linear discriminant Analysis)
       FA (Factor Analysis)
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
       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.
Statistical Approaches
   Information Theory
       Kullback relative information (Kullback
        divergence): maximum likelihood face detection
   SVM (Support Vector Machine)

   Bayes’ Decision Rule
Comparative Evaluation
   How does one count correct detection and false
   What is the system’s ROC curve?
   What is the size of the training set and how is
    training implemented?
   What is the face?
 Biometric identification
 Video conferencing
 Indexing of image and video database
 Intelligent human-computer interface

   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?

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