Face Detection and Modeling for Recognition - Biometrics Research ...

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Acknowledgments First of all, I would like to thank all the individuals who have helped me during my Ph.D. study at Michigan State University. I would like to express my deepest gratitude to my advisor, Dr. Anil K. Jain, for his guidance in academic research and his support in daily life. He broadened my view in research areas, especially in pattern recognition and computer vision, and taught me how to focus on research problems. I will never forget his advice “Just do it,” while being caught in multiple tasks at the same time. I am grateful to my Ph.D. committee, Dr. Mohamed Abdel-Mottaleb, Dr. George Stockman, Dr. John J. Weng, and Dr. Sarat C. Dass, for their valuable ideas, suggestions, and encouragement. I would also like to thank Dr. Chaur-Chin Chen and Dr. Wey-Shiuan Hwang for their help at the beginning of my study at MSU, and Dr. Shaoyun Chen and Yonghong Li for their help in the NASA modeling project. I am very grateful to Dr. Helen Shen and Dr. Mihran Tuceryan for their numerous suggestions and discussions on model compression. Special thanks are due to Philips Research-USA for offering me summer internships in 2000 and 2001; to Dr. Mohamed Abdel-Mottaleb for his guidance and suggestions in my work on face detection; to Dr. Patrick Flynn vi
for providing the range datasets; to Dr. Wey-Shiuan Hwang for providing his face recognition software; and to Dennis Bond for his help in creating a graphical user interface for face editing. Thanks are also due to Cathy M. Davison, Linda Moore, Starr Portice, and Beverly J. Wallace for their assistance in the administrative tasks. Special thanks to all the Prippies: Lin Hong, Aditya Vailaya, Nicolae Duta, Salil Prabhakar, Dan Gutchess, Paul Albee, Arun Ross, Anoop Namboodiri, Silviu Minut, Umut Uludag, Xiaoguang Lu, Martin Law, Miguel Figueroa-Villanue, and Yilu Zhang for their help during my stay in the PRIP Lab in the Department of Computer Science and Engineering at MSU. I would also like to thank Michael E. Farmer for giving me an opportunity to work on human tracking research. I would like to thank Mark H. McCullen for mentoring me to be a teaching assistant in CSE232; Dr. Jeffrey A. Fessler at the University of Michigan, Ann Arbor, for his valuable help during my transfer to the Dept. of Computer Science at Michigan State University; and Dr. Yung-Nien Sun and Dr. Chin-Hsing Chen in Taiwan for their encouragement and spiritual support. Special thanks to NASA, Philips Research-USA, Eaton corporation, and ONR (grant NO. N00014-01-1-0266) for their financial support during my Ph.D studies. Finally, but not the least, I would like to thank my parents, my wife, Dr. Pei-jing Li, and my son, Alan, for all the happiness they have shared with me. vii
Page 1 and 2: Face Detection and Modeling for Rec
Page 3 and 4: perimental results demonstrate succ
Page 5: To my parents; my lovely wife, Pei-
Page 9 and 10: 4 Face Modeling 97 4.1 Modeling Met
Page 11 and 12: List of Figures 1.1 Applications us
Page 13 and 14: 2.6 A breakdown of face recognition
Page 15 and 16: 4.2 3D triangular-mesh model and it
Page 17 and 18: 5.14 Fine alignment using geodesic
Page 19 and 20: Chapter 1 Introduction In recent ye
Page 21 and 22: (a) (b) Figure 1.1. Applications us
Page 23 and 24: (e) Figure 1.1. (Cont’d). (f) (a)
Page 25 and 26: esolutions and of poor quality (i.e
Page 27 and 28: can recognize known faces in carica
Page 29 and 30: the motivation for studies that att
Page 31 and 32: (a) Figure 1.10. Similarity of fron
Page 33 and 34: verify the face present in an image
Page 35 and 36: 17 Figure 1.12. System diagram of o
Page 37 and 38: algorithm can also provide geometri
Page 39 and 40: commercial parametric face modeling
Page 41 and 42: 1.5.1 Face Alignment Using 2.5D Sna
Page 43 and 44: 1.5.3 Face Alignment Using Interact
Page 45 and 46: Using merely low-level features (e.
Page 47 and 48: such as eyes, mouth and face bounda
Page 49 and 50: can be applied to a 3D face model w
Page 51 and 52: Chapter 2 Literature Review We firs
Page 53 and 54: mation theory, geometrical modeling
Page 55 and 56: (e) (f) (g) (h) (i) (j) (k) (l) Fig
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esult in different recognition appr
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(a) (b) (c) Figure 2.2. Examples of
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(a) (b) (c) (d) Figure 2.4. Interna
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Figure 2.6. A breakdown of face rec
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2.3 Face Modeling Face modeling pla
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An advanced modeling approach which
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tionals to minimize, implementation
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are listed in Table 2.2. All of the
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low-level features can provide mean
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holistic 2D and geometrical 3D feat
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size) to generate potential face ca
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oundary. A face score is computed f
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normalized red-green (r-g) space [1
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(a) (b) Figure 3.3. The Y C b C r c
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(a) Figure 3.5. 2D projections of t
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3.3 Localization of Facial Features
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(a) (b) (c) Figure 3.9. Constructio
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The eye map from the chroma is enha
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mouth is performed within the face
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Figure 3.12. Computation of face bo
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center, and lengths of major and mi
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segment. The other term favors an u
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(a) (b) (c) (d) (e) Figure 3.15. Fa
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(a) (b) (c) (d) (e) Figure 3.18. Fa
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The number of false positives is al
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(a) (b) (c) (d) Figure 3.20. Face d
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Figure 3.22. Face detection results
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Figure 3.22. (Cont’d). 93
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3.5 Summary We have presented a fac
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Chapter 4 Face Modeling We first in
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and nose in frontal views), and bec
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4.3 Facial Measurements Facial meas
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4.4 Model Construction Our face mod
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a feature-dependent decay factor. F
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is to find appropriate external ene
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(a) (b) (d) (e) (f) Figure 4.11. Te
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Chapter 5 Semantic Face Recognition
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(a) (b) (c) Figure 5.1. Semantic fa
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of Fourier series truncation. In ad
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ponent color. The semantic facial s
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(a) (b) (c) (d) Figure 5.5. Boundar
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(a) (b) (c) (d) Figure 5.7. Coarse
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In the Baysian framework, given an
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edge map; hence it requires a clean
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shows examples of mouth energies. F
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[ ( ∂Φ ∂t =∇Φ µ 1 div(g
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(a) (b) (c) (d) (e) (f) Figure 5.14
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y the reliability of component matc
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tation, face size, and lighting con
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(a) (b) (c) (d) Figure 5.18. Exampl
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(a) (b) (c) (d) (e) (f) (g) (h) (i)
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(a) (b) (c) (d) (e) (f) (g) Figure
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5.6 Summary For overcoming variatio
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such vision-based systems are (i) d
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6.2 Future Directions Based on the
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(a) (b) (c) (d) Figure 6.2. An exam
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• Non-frontal training views: Acc
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Appendices
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⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ Y
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and are shown as blue-dashed lines
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adius of a cluster ‘i’ w.r.t. a
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Appendix C Image Processing Templat
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considering pixel classes as voxel
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gray8imageA(120,120) = 200; // Asse
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Bibliography [1] Visionics Corporat
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[32] L. Wiskott, J.M. Fellous, N. K
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[59] M. Grudin, “On internal repr
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[84] M. Abdel-Mottaleb and A. Elgam
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[113] B. Kim and P. Burger, “Dept
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[141] M.D. Adams and F. Kossentini,
Page 197 and 198:
[169] P.T. Jackway and M. Deriche,
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