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Automated Facial Feature Points Extraction

Author(s): Arun Kumar Nagdeve | Somesh Kumar Dewangan

Journal: International Journal of Computer & Electronics Research
ISSN 2320-9348

Volume: 1;
Issue: 3;
Start page: 111;
Date: 2012;
Original page

Keywords: Object | corner end point | linear search | cumulative histogram | gray level co-occurrence matrices (GLCM) | face recognition | corelation (Corr) | angular second moment (ASM) | entropy | maximum probablity | inverse difference(ID) | inverse difference moment (IDM)

This paper proposes the extraction of geometric and texture feature of face automatically from front view. For extracting the geometric feature cumulative histogram approach is used and co-occurrence matrices are used to extract the texture feature of face. From the input image, face location is detected using the viola-Jones algorithm, from the structure of human face, 4 relevant regions such as left eye, right eye, nose and mouth regions are cropped named as object. For extracting the geometric feature  histogram of each Object is computed and its cumulative histogram values are employed by varying different threshold values to create a new filtered binary image of each Object, then the corner end-point of each object (binary image) is detected using the linear search technique. For extracting the texture feature co-occurrence matrices of each object is determined, using this matrix, angular second moment, entropy, maximum probability of occurrence pixels, inverse difference, inverse difference moment , mean, contrast of each object is detected.
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