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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/2650

    Title: Face Recognition Base on Gini Features and K-L Transform
    Authors: LIU Zhong-rong
    Contributors: Department of Bioinformatics
    Keywords: face recognition;K-L transform;Gini;Otsu
    Date: 2009
    Issue Date: 2009-11-06 22:33:26 (UTC+8)
    Publisher: Asia University
    Abstract: In recent years, face recognition was attractive by the public's
    attention, and the application of face recognition has become
    increasingly widespread; about these applications can be divided into
    two parts ---human face detection and human face recognition .The
    point of face detection is to identify the key parts of face, and then
    mark or capture it. Face recognition is to capture images of human
    faces and do the matching with the face images ins ide the
    database and then distinguish the identity. This article is part of
    face recognition.
    In 1991 the method of human face recognition that use the
    principal component analysis have been proposed, However, this
    approach is to calculate the variance of the whole image. The compute
    capacity is too large and the pace of the calculation is too slow.
    Therefore, the research of this essay is to check the characteristics by
    the unevenness of the image (Gini), and then use this as a template to
    identify Hope to enhance the recognition speed and rate. At the same
    time, in order to have an objective standard to determine whether the
    recognition is correct or not, we use the Otsu group method to
    divide groups by the European several Reed distances which were
    got by the K-L transform.
    In this essay, we used the ORL face database, which have 40
    people, and each person has 10 images. There are totally 400 images
    of 112 ? 92 in it. We used everyone's first image as a template, when
    only using K-L transform. The average time of recognition was about
    4.12 seconds, the average recognition rate was 88.65%, and then we
    sampled Gini value--- 0.79 ~ 0.81, and use KL transform. The average
    time of recognition was about 1.81 seconds, and the average
    recognition rate became 89.25 percent. If the Gini value is set to 0.77 ~
    0.81, the average recognition rate can be increased to 90.91%, the
    average time was 2.06 seconds. Here we can find, the recognition rate
    is not significantly improved, but the speed of recognition gets very
    good effect.
    Appears in Collections:[生物資訊與醫學工程學系 ] 博碩士論文

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