KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Jaturawat, PhichayaSubject: search.filters.subject.Factors

Search Results

Now showing 1 - 1 of 1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Effect of variation factors on the processing time of the face recognition algorithms in video sequence
    (2016-12-23)
    Phankokkruad, Manop
    ;
    Jaturawat, Phichaya
    Face recognition is one of the most popular research topics of the biometric identification. Sometimes, face recognition work under the unstable environments. Therefore, the study on the factors that affect face recognition performance is the very interesting topic. The recognition accuracy can be affected by many factors, but the quality of image database and system environmental factors, are mainly factors that need to be focused. This research creates the image database to represent each user by controlled the environmental factors strictly, to ensure that the images have minimum confounding and have the best quality. The performance of the processing was tested by the variation of factors. These are variant variables that using for testing the algorithms accuracy and processing speed. The standard processing times are done on the still images. In the video, the experiment was conducted by using the test set with variation of confounding factors. All factors are similar to the still images, but it has one addition factors that is video resolution. The results found that, the face recognition processing time, the factors that are face recognition algorithms, video resolution, and the number of input image affected to the processing speed. As results from the experiment, it is relevant for the hypothesis that is the smaller number of input image, and smaller image size can reduce the processing time. In the case of face recognition algorithm, Fisherfaces is the fastest algorithms which took a shorter processing time than Eigenfaces, and LBPH, respectively. The results from this experiment can indicate the most effective face recognition algorithm. After that, it would be applied for the as-is sys- tem.