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

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    Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms
    (2017-08-29)
    Phankokkruad, Manop
    ;
    Jaturawat, Phichaya
    Face recognition is a personal identification method using biometrics that is gaining the attention in this research field. The face recognition process can be done without the human and devices interaction, so it can be applied in several applications. In additions, the face recognition systems are typically implemented at different places in unconstrained environments. Hence, the study of the factors that impact the face recognition accuracy is an interesting and challenging topic. In the class attendance checking system using face recognition, there are variations of three factors that possibly affect the accuracy of the system; facial expressions, and face viewpoints. This study intends to compare facial recognition accuracy of three well-known algorithms namely Eigenfaces, Fisherfaces, and LBPH. The experiments conducted in the respects of the variation of facial expressions, and face viewpoints in the actual classroom. The results of the experiment demonstrated that LBPH is the most precise algorithm which achieves 81.67% of accuracy in still-image-based testing. The facial expression that has the most impact on accuracy is the grin, and face viewpoints that affect accuracy are looking down and tilting left, and right respectively. Therefore, LBPH is the most suitable algorithm to apply in a class attendance checking system after considering the accuracy.
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    An evaluation of face recognition algorithms and accuracy based on video in unconstrained factors
    (2017-04-05)
    Jaturawat, Phichaya
    ;
    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
    ;
    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.