Object Recognition

    Manifold Learning for Object/Face Recognition

    The aim of this research is to recognize objects/faces irrespective of the pose and orientation. To recognize an object, it is sufficient to identify the manifold to which an object belongs. Our method focuses on modeling the manifolds effectively. For example, faces of the same person lie on the same manifold irrespective of the pose variations.

    Face Recoginition

    Manifold Learning
    When datapoints are represented in a higher dimensional space, it is observed that objects of the same category tend to lie relatively close to each other, which results in a geometric structure in a higher dimensional space. This geometric structure is called a manifold.

    Manifold Learning

    As there exists a manifold corresponding to every object/face, to recognize an object/face it is sufficient to identify the manifold to which the object/face belongs. Our algorithm aims at modeling the manifold to deal with the general object and the face recognition problems.

    Object/face recognition is a challenging problem in computer vision. Some factors that make it a difficult problem are

    • View point
    • Illumination
    • Occlusion
    • Translation & rotation

    Popular methods in object recognition are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF). Popular methods in face recognition are PCA, MPCA, LDA, etc. We propose a novel approach for modeling the manifolds and using them for recognition.

    Object Recognition

    Proposed Method
    We propose a robust mathematical model for representing complex manifolds formed by face/object images and a method to map test images to these manifolds.

    Schematic of Steps of Algorithm

    Training Phase

    Schematic of Steps of Algorithm

    Testing Phase

    The method was tested and evaluated for face recognition. The results show that the proposed method is superior to other state of the art methods.

    Charts of Results

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    Vision Lab, Dr. Vijayan Asari, Director

    Kettering Lab 461-464 
    300 College Park 
    Dayton, Ohio 45469 - 0232