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Author: search.filters.author.Juhong, AniwatSubject: search.filters.subject.Face recognition

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    Biometrics based on facial landmark with application in person identification
    (2019-01-01)
    Juhong, Aniwat
    ;
    Purahong, Boonchana
    ;
    Suwan, Supakorn
    ;
    Pitavirooj, Chuchart
    This paper presents a novel technique for face recognition based on facial landmarks extracted automatically. Our landmarks are those associated with eyes mouth and nose. To extract facial landmarks, we first use Haar cascade algorithm to detect the face ROI following by Haar cascade algorithm for the eye, mouth and nose ROI determination. To find landmark associated with the eye, we convert eye ROI image to binary image using thresholding algorithm. To exclude the eyebrow region, we apply horizontal radon transform. The project data will then be used to separate the eyebrow region from the eye region. To detect eye-related landmark, vertical radon transform is applied. With the vertical projection data, the outermost pixel can be identified and the associated eye landmark can be determined. The similar technique can then be used to identify landmarks associated with the nose and mouth area. Given the correspond landmarks on the reference face and the query face, geometric transformation can be determined using normal equation bases on minimized mean squared error. The two faces are then aligned. To provide the quantitative measurement, the two aligned face are converted to edge image using canny edge algorithm. The distance map error between the two aligned edge facial images is then used to identify the query face. The purposed algorithm for person identification was tested on the face database resulting in a very high accuracy.
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    Face recognition based on facial landmark detection
    (2017-12-19)
    Juhong, Aniwat
    ;
    Pintavirooj, C.
    This paper presents a novel technique for face recognition based on facial landmarks extracted automatically. Our landmarks are those associated with eyes mouth and nose. With the extracted landmarks, the area triplets and the associated geometric invariance are formed. We opt to use area and triangle confined within the triangle as the invariance. To bypass the perspective constraints, we take the face image with high focal length and at the farther distance. Orthogonal projection and Euclidean transformation are then assumed. As area is relative invariance under Euclidean transformation, the absolute area ratios between consecutive area triples are applied. Our purposed algorithm is tested successfully to identify person and could be a promising technique for facial recognition.