(Summary) A Fusion Face Recognition Approach based on 7-Layer Deep Learning Neural Network

Posted on Posted in Artificial Intelligent, Computer Science, Machine Learning

Title : A Fusion Face Recognition Approach based on 7-Layer Deep Learning Neural Network

Author : Jianzheng Liu, Chunlin Fang, and ChaoWu

Research Object : Face Recognition

  • Why Face
  • Why Face Recognition

Available methods and Advantages and Disadvantages

  • Lu et al Method (Discriminative Multi-Manifold Analysis/DMMA)
    • (+) Training is just only one sample face image. Most of Face Recognition training methods aren’t.
  • Yang et al Method (Fast 1-minimization algorithm)
    • (+) Speed and Scalability
    • (+) new solution based on classical convex optimization framework (Augmented Lagrangian Methods)
    • (+) Viable solution to real-world  & time-critical
  • Liao et al (Aligment-free approach)
    • (+) Partial face recognition
    • (+) no face aligment to recognize by eye coordinate or any fiducial points
  • Wagner et al (Simple face recognition system)
    • High degree of robustness
    • Stability to illumination variation, image misalignment & partial occlusion.

 

Problem with selected method

  • Fusion Feature
    • MHI Algorithm for psychological face expression.
    • Aligned Gray Image (Face Detection result with Haar Cascade method) and interpolated to 100×100 image.
  • Problem of Fusion Feature that have about 20.000 feature generated, still difficult to classified by casual machine learning.

Solution for selected method

  • Deep Learning Neural Network (7-Layer). Have advantages to reduce dimension code from high dimensional data to low dimensional codes that called auto-encoder in 6 first layer with DBN (Deep Belief Net). DBN is component of Deep Learning.