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Title: Novel methods on Facial images for age classification and person identification
Researcher: Miryala, Chandra Mohan
Guide(s): Vakulabharanam, Vijaya Kumar
Keywords: Person Identification, Wavelets, Facial Skin, Face Recognition, Human Face
Upload Date: 25-Aug-2011
University: Jawaharlal Nehru Technological University
Completed Date: April 2010
Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially in the recent past. There are at least two reasons for this trend; first is the wide range of commercial and law enforcement applications such as smart cards, access control, passports, credit cards, driving licenses, biometric authentication, video surveillance, and information security, among others and the second is the availability of feasible technologies after 30 years of research. The thesis mainly concentrated on textural properties of skin for face recognition and age classification. Initially facial properties are derived by exploring statistical texture features (STF) of co-occurrence matrices. A precise Face recognition is carried out on STF by a new distance function scheme that eliminates retrieved facial images based on significant texture features. One of the major drawback of most of the Face Recognition Methods are, they fail in recognizing the humans, if there is a variation in terms of age between probe and database facial image, because there will be significant changes in the face when a person acquires age. For this, a new direction for the child and adult classification using texture features derived from geometric properties of human face is proposed. The texture features of the present approach are computed from facial distance features. newlineThe advantage of the proposed approach is, it can be effectively used for persons with folded eye, blind, wearing spectacles, and face images with closed eyes. Generally the bone structural changes do not occur after the person is fully grown that is the geometric relationships of primary features do not vary. To extend the age classification problem further, secondary features based on Topological Texture Features in the facial skin are identified and exploited in the present study.
Pagination: xx, 155p.
Appears in Departments:Faculty of Computer Science & Engineering

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01_title.pdfAttached File138.12 kBAdobe PDFView/Open
02_declaration.pdf91.25 kBAdobe PDFView/Open
03_certificate.pdf116.03 kBAdobe PDFView/Open
04_acknowlegements.pdf96.52 kBAdobe PDFView/Open
05_abstract.pdf104.76 kBAdobe PDFView/Open
06_contents.pdf120.88 kBAdobe PDFView/Open
07_list of figures.pdf128.08 kBAdobe PDFView/Open
08_list of tables.pdf143.14 kBAdobe PDFView/Open
09_list of abbreviations.pdf123.99 kBAdobe PDFView/Open
10_chapter 1.pdf212.4 kBAdobe PDFView/Open
11_chapter 2.pdf821.38 kBAdobe PDFView/Open
12_chapter 3.pdf1.07 MBAdobe PDFView/Open
13_chapter 4.pdf770.51 kBAdobe PDFView/Open
14_chapter 5.pdf1.04 MBAdobe PDFView/Open
15_chapter 6.pdf251.25 kBAdobe PDFView/Open
16_chapter 7.pdf175.39 kBAdobe PDFView/Open
17_references.pdf286.75 kBAdobe PDFView/Open

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