LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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Category Exemplar application scenarios Face ID Driver licenses, entitlement programs, immigration, national ID, passports , voter registration, welfare registration Access control Bordercrossing control, facility access, vehicle access, smart kiosk and ATM, computer access, computer program access, computer network access, online program access, online transactions access, long distance learning access, online examinations access, online database access Security Terrorist alert, secure flight boarding systems, stadium audience scanning, computer security, computer application security, database security, file encryption, intranet security, Internet security, medical records, secure trading terminals Surveillance Advanced video surveillance, nuclear plant surveillance, park surveillance, neighborhood watch, power grid surveillance, CCTV control, portal control Smart cards Stored value security, user authentication Law enforcement Crime stopping and suspect alert, shoplifter recognition, suspect tracking and investigation, suspect background check, identifying cheats and casino undesirables, postevent analysis, welfare fraud, criminal face retrieval and recognition Face databases Face indexing and retrieval, automatic face labeling, face classification Multimedia management Facebased search, facebased video segmentation and summarization, event detection Human computer interaction Interactive gaming, proactive computing (HCI) Others Antique photo verification, very low bitrate image & video transmission, etc. 42
CHAPTER VIII FOR PROSPECTIVE RESEARCHERS Automatic recognition is a vast and modern research area of computer vision and Artificial intelligence, reaching from recognition of faces, facial expressions analysis, facial geometry, building successful face model, automatically detecting, locating and tracking faces, as well as extraction of face orientation and facial features. The problem of machine recognition of human faces continues to attract researchers from disciplines such as image processing, pattern recognition, neural networks, computer vision, computer graphics, and psychology. This Research Front on Face Recognition from the field of Computer Science was selected for mapping from the list of Top Topics for June 2008. The map is a diagrammatic representation of the 33 core papers comprising the front in Computer Science. Each circle represents a highly cited paper. The lines between circles represent the strongest cocitation links for each paper that is, indicating that the papers are frequently cited together. Papers close to each other on the map are generally more highly cocited. The most recent paper(s) are indicated in dark. 43
Page 1 and 2: LITERATURE SURVEY OF AUTOMATIC FACE
Page 3 and 4: ABSTRACT There are three fundamenta
Page 5 and 6: 2.3.2 Processing Steps before Face
Page 7 and 8: LIST OF TABLES Table Page 5.2.1 The
Page 9 and 10: 12.1. Functioning principle of the
Page 11 and 12: the brain recognizes a face has lon
Page 13 and 14: CHAPTER II HOW A COMPUTER CAN PERFO
Page 15 and 16: Fig.2.2. Process of face recognitio
Page 17 and 18: non convex variations of face manif
Page 19 and 20: 3.1 Some Major difficulty for AFR C
Page 21 and 22: Gender researchers found surprising
Page 23 and 24: The idea of this method comes from
Page 25 and 26: Fig.4.3.1. Principal Components (PC
Page 27 and 28: and orientations (phase, amplitude)
Page 29 and 30: 4.14 Bayesian Framework A probabili
Page 31 and 32: feature line of that class. The que
Page 33 and 34: oughly the same scale), scale norma
Page 35 and 36: Table 5.2.4 Result for combined dat
Page 37 and 38: performance of the AutoAssociatio
Page 39 and 40: Furthermore, three of the seven alg
Page 41: Table 6.1 Various application of au
Page 45 and 46: Table 7.1 Internet Resources for Re
Page 47 and 48: etween the computer vision, signal
Page 49 and 50: So, in order to reconstruct the ori
Page 51: CHAPTER XII EIGENVECTORS AND EIGENV
Page 54 and 55: Step 4: Calculate the eigenvectors
Page 56 and 57: CHAPTER XV CLASSIFYING THE FACES Th
Page 58 and 59: CHAPTER XVII INTRODUCTION Images ar
Page 60: CHAPTER XIX THE ALGORITHM As in all
Page 63 and 64: If the condition Zmin < Zmed < Zmax
Page 65 and 66: (a) (b) Fig 21.3: (c) The image wit
Page 67 and 68: [5] M. Yang, D. J. Kriegman, and N.
Page 69: [25] Adaptive median filters: new a

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