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Author: search.filters.author.Phankokkruad, ManopSubject: search.filters.subject.Face recognition

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    An evaluation of face recognition algorithms and accuracy based on video in unconstrained factors
    (2017-04-05)
    Jaturawat, Phichaya
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    Phankokkruad, Manop
    Face recognition is the biometric personal identification that gaining a lot of attention recently. This method has the ability to identify a person from still image and video by using human face. For the accurate recognition, algorithm and reference database needs to be concerned. However, in the practical system have many external factors that affect to the recognition accuracy differently for each algorithm. This is a challenge problem of class attendance recording system deployment, which has uncontrolled environments. This paper comparing three well known algorithm that are Eigenfaces, Fisherfaces, and LBPH by adopts our new database that contains a face of individuals with variety of pose and expression. The experiment of face recognition in video conducted by varied the external factors that are light exposure, noise, and the video resolution, in the possible range. The results showed LBPH got the highest accuracy in all experiments, but this algorithm has the higher impact of the negative light exposure and high noise level more than the others that are statistical approach.
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    Impact of facial expressions and posture variations in face recognition rate on different image databases
    (2017-01-01)
    Jaturawat, Phichaya
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    Phankokkruad, Manop
    In this paper, the impact of facial expression and posture variations in face recognition were studied by using three face recognition algorithms that are Eigenfaces, Fisherfaces, and LBPH in terms of recognition accuracy. In order to find the type of algorithms that works efficiently for face recognition in video. The experiment was conducted by using two different databases with three amounts of image in training set. DB-one is uncontrolled people in the images, and DB-two is controlled facial expressions and posture. The results show the facial expression and posture variations have a lot of impact to Eigenfaces and Fisherfaces and the LBPH got the impact less than the others. It concluded that the impact of facial expression and postures are different on each algorithm, and impacted to the recognition accuracy.