Content-based image retrieval that retrieves images based on their visual content has expanded rapidly in recent years and is
transformed into an important research issue. One of the main branch image retrieval is retrieval of human face that its aim is to explore
and extract facial images in a database that match a reference image or a series of key words. Face recognition is very useful in security
issues compared to other biometric components due to no need for cooperation. That's why a lot of research has been done on a variety of
face recognition methods. Changes in illumination, occlusion and pose are the main problems that researchers have ever faced. While the
issue of aging, which is one of the important issues, especially on documents, passports and police records is less considered compared to
other factors effective on face recognition. This article has dealt with this issue. In this paper the framework of AIFR that includes three
main stages of feature extraction, dimension reduction and face matching has been examined. The latest methods of AIFR have been
reviewed and suggestions are provided for the future works.
Published In:IJCSN Journal Volume 6, Issue 5
Date of Publication : October2017
Pages : 588-593
Figures :11
Tables : 02
Seyyed Mohammad Hossein Dadgar : is a master researcher in
computer science at the university of azad central tehran branch.
His research interests include computer vision, neural networks,
and image captioning. he received a bachelor in hardware
engineering from Isfahan university of technology, a masters in
computer science in 2009 and 2016, respectively.
Dr.V.Sharma : born in Aligarh, UP. Worked as a Professor (CSE),
in Aligarh Muslim University,Aligarh, Uttar- Pradesh, India. He
Guided Number of Ph.D Scholars in Aligarh Muslim University
& Published number of Journals. Research interest includes
Software Engineering, Networks and Testing Methodologies
& Published number of Journals.
Hanie Maleki : is a Master student in Computer Science at the
University of Azad Central Tehran branch, Iran. Her research
interests include Image Processing, face and object detection and
classification, attributes learning, neural networks, and image
captioning. She received a Bachelor in Software Engineering from
Iran Khayyam University, in 2012.
Image retrieval, Biometric, Face recognition, Aging
Face recognition can be considered recognition of a very
complex object, where the detected object is the face.
Solving this problem is even more strict, because search
for objects is done among objects belonging to the same
class. Moreover, in most cases, more than one visible
image is not available to train the system and various
problems arise when the images were not obtained under
controlled conditions. In this study it was aimed to review
the challenges, concepts and methods provided for AIFR.
[1] Andrea F. Abate, Michele Nappi, Daniel Riccio, Gabriele
Sabatino, “2D and 3D face recognition: A survey”,
Pattern Recognition Letters, vol. 28, no. 14, pp.1885–
1906, 2007.
[2] Adini. Y, Moses. Y, Ullman. S,”Face recognition: The
problem of compensating for changes in illumination
direction”, IEEE Trans. Pattern Anal. Machine Intell, vol.
19, no. 7, pp.721–732, 1997.
[3] Gao.Y, Leung. M.K.H, “Face recognition using line edge
map”, IEEE Trans. Pattern Anal. Machine Intell, vol. 24,
no. 6, pp.764–779, 2002.
[4] Belhumeur, Peter N., Hespanha, Joa˜o P., Kriegman,
David, “Eigenfaces vs. fisherfaces: Using class specific
linear projection”, IEEE Trans. Pattern Anal. Machine
Intell, vol. 19, no. 7, pp.711–720, 1997.
[5] Okada. K, von der Malsburg. C, “Pose-invariant face
recognition with parametric linear subspaces”, In: Fifth
IEEE Internat. Conf. on Automatic Face and Gesture
Recognition, pp. 64–69, 2002.
[6] Gross. R, Matthews. I, Baker. S, “Eigen light-fields and
face recognition across pose”, In: Proc. Fifth IEEE
Internat. Conf. on Automatic Face and Gesture
Recognition, pp. 1–7, 2002.
[7] Martinez. A.M, “Recognizing imprecisely localized,
partially occluded, and expression variant faces from a
single sample per class”, IEEE Trans. Pattern Anal.
Machine Intell, vol. 24, no, pp.748–763, 2002.
[8] Kurita. T, Pic. M, Takahashi. T, “Recognition and
detection of occluded faces by a neural network classifier
with recursive data reconstruction”, In: IEEE Conf. on
Advanced Video and Signal Based Surveillance, pp. 53–
58, 2003.
[9] Sahbi, Hichem, Boujemaa, Nozha, “Robust face
recognition using dynamic space warping”, In: Biometric
Authentication, Internat. ECCV 2002 Workshop
Copenhagen, pp. 121–132, 2002.
[10] Phillips. J.P, Moon. H, Rizvi. A.S, Rauss. P.J, “The
FERET evaluation methodology for face-recognition
algorithms”, IEEE Trans. Pattern Anal. Machine Intell,
vol. 22, no. 10, pp. 1090–1104, 2000.
[11] Sim. T, Baker. S, Bsat. M, “The CMU pose, illumination,
and expression database”, IEEE Trans. Pattern Anal.
Machine Intell, vol. 25, no. 12, pp. 1615–1618, 2003
[12] Martinez. A.M, “Recognizing imprecisely localized,
partially occluded, and expression variant faces from a
single sample per class”, IEEE Trans. Pattern Anal.
Machine Intell, vol. 24, no. 6, pp. 748–763, 2002.
[13] Xin Geng, Zhi-Hua Zhou, and Kate Smith-Miles,
“Automatic age estimation based on facial aging
patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol.
29, no. 12, pp. 2234–2240, 2007.
[14] Guodong Guo, Yun Fu, Charles R. Dyer, and Thomas S.
Huang, “Image-based human age estimation by manifold
learning and locally adjusted robust regression,” IEEE
Transactions on Image Processing, vol. 17, no. 7, pp.
1178–1188, 2008.
[15] Young H. Kwon and Niels Da Vitoria Lobo, “Age
classification from facial images,” in In Proc. IEEE Conf.
Computer Vision and Pattern Recognition, 1999, pp.
762–767.
[16] Narayanan Ramanathan and Rama Chellappa, “Face
verification across age progression,” IEEE Transactions
on Image Processing, vol. 15, no. 11, pp. 3349–3361,
2006.
[17] Jin-Li Suo, Song Chun Zhu, Shiguang Shan, and Xilin
Chen, “A compositional and dynamic model for face
aging,” IEEE Transactions on Pattern Anal. Mach. Intell.,
vol. 32, no. 3, pp. 385–401, 2010.
[18] Jin-Li Suo, Xilin Chen, Shiguang Shan, andWen Gao,
“Learning long term face aging patterns from partially
dense aging databases,” in International Conference on
Computer Vision , 2009, pp. 622–629.
[19] Norimichi Tsumura, Nobutoshi Ojima, Kayoko Sato,
Mitsuhiro Shiraishi, Hideto Shimizu, Hirohide
Nabeshima, Syuuichi Akazaki, Kimihiko Hori, and
Yoichi Miyake, “Image based skin color and texture
analysis/synthesis by extracting hemoglobin and melanin
information in the skin,” ACM Trans. Graph., vol. 22, no.
3, pp. 770–779, 2003.
[20] D. Gong, Z. Li, D. Lin, J. Liu, X. Tang, “Hidden Factor
Analysis for Age Invariant Face Recognition”, in
International Conference on Computer Vision , 2013.
[21] D. Gong, Z. Li, D. Tao, J. Liu, X. Li, “A Maximum
Entropy Feature Descriptor for Age Invariant Face
Recognition”, Conference on Computer Vision and
Pattern Recognition, 2015.
[22] Unsang Park, Yiying Tong, and Anil K. Jain, “Ageinvariant
face recognition,” IEEE Trans. Pattern Anal.
Mach. Intell., vol. 32, no. 5, pp. 947–954, 2010.
[23] Zhifeng Li, Unsang Park, and Anil K. Jain, “A
discriminative model for age invariant face recognition,” IEEE Transactions on Information Forensics and
Security, vol. 6, no. 3-2, pp. 1028–1037, 2011.
[24] D. Bouchaffra,” Nonlinear Topological Component
Analysis: Application to Age-Invariant Face
Recognition”, IEEE TRANSACTIONS ON NEURAL
NETWORKS AND LEARNING SYSTEMS, 2014.
[25] B-C. Chen, C-S. Chen, and W. H. Hsu, “Cross-Age
Reference Coding for Age-Invariant Face Recognition
and Retrieval”, Springer International Publishing
Switzerland, pp.768-783, 2014.
[26] M. Turk, A. Pentland, “Eigenfaces for Recognition,
Journal of Cognitive Neurosicence”, Vol. 3, No. 1, pp.
71-86, 1991.
[27] Peter N. Belhumeur, Jo ˜a o P. Hespanha, and David J.
Kriegman, “Eigenfaces vs. fisherfaces: Recognition using
class specific linear projection,” IEEE Trans. Pattern
Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720, 1997.
[28] T.Ojala, M.Pietik, T.Maenpa, “Multiresolution gray-scale
and rotation invariant texture classification with local
binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 24, no. 7, pp. 971–987, 2002.
[29] T.Ahonen, A.Hadid, M. Pietikäinen, “Face recognition
with local binary patterns”, Springer Berlin Heidelberg,
pp. 469-481, 2004.
[30] T.Ahonen, A.Hadid, M. Pietik, “Face description with
local binary patterns: Application to face recognition,”
IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12,
pp. 2037–2041, 2006.
[31] D. G. Lowe, "Distinctive image features from scaleinvariant
keypoints," International journal of computer
vision, vol. 60, pp. 91-110, 2004.
[32] E.Takikawa, S.Lao, M.Kawade, B-L.Lu, “PERSONSPECIFIC
SIFT FEATURES FOR FACE
RECOGNITION”, IEEE International Conference on
Acoustics, Speech and Signal Processing, 2007.
[33] N. Dalal and B. Triggs, "Histograms of oriented gradients
for human detection," in Computer Vision and Pattern
Recognition, pp. 886-893, 2005.
[34] A.Albiol, D.Monzo, A.Martin, J.Sastre, “Face recognition
using HOG–EBGM”, Pattern Recognition Letters, vol.29,
no.10, pp.1537-1543, 2008.
[35] M.B. Stegmann, “The AAM-API: An Open Source
Active Appearance Model Implementation,” Proc. Int’l
Conf. Medical Image Computing and Computer-Assisted
Intervention, pp. 951-952, 2003.
[36] T.F. Cootes, G.J. Edwards, and C.J. Taylor, “Active
Appearance Models,” IEEE Trans. Pattern Analysis and
Machine Intelligence, vol. 23, no. 6, pp. 681- 685, June
2001.
[37] M. Turk and A. Pentland, “Face recognition using
eigenfaces”, In Computer Vision and Pattern
Recognition, pages 586–591, 1991.
[38] X. Wang and X. Tang, “A unified framework for
subspace face recognition”, IEEE Trans. Pattern Anal.
Mach. Intell., vol. 26, no. 9, pp. 1222–1228, 2004.
[39] “FaceVACS Software Developer Kit, Cognitec Systems
GmbH,” http:// www.cognitec-systems.de, 2010.
[40] Karl Ricanek Jr. and Tamirat Tesafaye, “Morph: A
longitudinal image database of normal adult ageprogression,”
in FG, 2006, pp. 341–345.
[41] FG-NET Aging Database, http://www.fgnet.rsunit.com/.