Experimental Evaluation of Age estimation using Facial feature extraction through CNN
Abstract
Human facial image processing has been keeping
researchers interested and baffled for years. Since human
faces provide a lot of information, this information has been
studied thoroughly since many topics have been related to this
information provided by human face. For proper Face
detection accuracy in facial feature extraction must be
maintained since it plays a crucial role in face alignment. It is
an obligatory processing for facial attributes detection. The
sense is to devise a feature-extraction course of action that can
furthermore be secondhand for feebleness estimation in real
survival applications.
Face gives away most of the information required to
estimate the age of a person. Hence the problem of age
estimation has been cut down by doing facial age estimation.
Business stuff, stake, human-cantered image conception, agespecific
avocation systems, protecting minors from adult web
sites and venues etc. compel directly or indirectly infirmity of
the user/client hereafter they bounce be eventual as
applications to Human Age Estimation (HAE). Since age
plays a major role in facial changes, it has come to the
forefront. Points are usually located on the corners, tips or mid
points of the facial features like nose, eyes, lips, etc.
Realization and detection of facial centerpiece points plays a
noteworthy role in manifold facial conception applications
relish video review, greet recognition, decrepitude grouping,
bathos classification, engage modeling, face anthropometric,
emotion euphemism, montage curio, and robotics.
Full Text:
PDFReferences
[1] Gil Levi and Tal Hassner, LATCH: Learned
Arrangements of Three Patch Codes, IEEE Winter
Conference on Applications of Computer Vision
(WACV), Lake Placid, NY, USA, March, 2016
[2] Gil Levi and Tal Hassner, Age and Gender
Classification using Convolutional Neural Networks,
IEEE Workshop on Analysis and Modeling of Faces and
Gestures (AMFG), at the IEEE Conf. on Computer
Vision and Pattern Recognition (CVPR), Boston, June
[3] Sun, Yi, Xiaogang Wang, and Xiaoou Tang. “Deep
learning face representation from predicting 10,000
classes.” Computer Vision and Pattern Recognition
(CVPR), 2014 IEEE Conference on. IEEE, 2014.
[4] Schroff, Florian, Dmitry Kalenichenko, and James
Philbin. “Facenet: A unified embedding for face
recognition and clustering.” arXiv preprint
arXiv:1503.03832 (2015).
[5] Kwon, Young Ho, and Niels Da Vitoria Lobo. “Age
classification from facial images.” Computer Vision and
Pattern Recognition, 1994. Proceedings CVPR’94., 1994
IEEE Computer Society Conference on. IEEE, 1994.
[6] Eidinger, Eran, Roee Enbar, and Tal Hassner. “Age
and gender estimation of unfiltered faces.” Information
Forensics and Security, IEEE Transactions on 9.12
(2014): 2170-2179.
[7] Gao, Feng, and Haizhou Ai. “Face age classification
on consumer images with gabor feature and fuzzy lda
method.” Advances in biometrics. Springer Berlin
Heidelberg, 2009. 132-141.
[8] Liu, Chengjun, and Harry Wechsler. “Gabor feature
based classification using the enhanced fisher linear
discriminant model for face recognition.” Image
processing, IEEE Transactions on 11.4 (2002): 467-476.
[9] Chao, Wei-Lun, Jun-Zuo Liu, and Jian-Jiun Ding.
“Facial age estimation based on label-sensitive learning
and age-oriented regression.” Pattern Recognition 46.3
(2013): 628-641.
[10] Taigman, Yaniv, et al. “Deepface: Closing the gap to
human-level performance in face verification.” Computer
Vision and Pattern Recognition (CVPR), 2014 IEEE
Conference on. IEEE, 2014.
[11] LeCun, Yann, et al. “Backpropagation applied to
handwritten zip code recognition.” Neural computation
4 (1989): 541-551.
[12] Russakovsky, Olga, et al. “Imagenet large scale
visual recognition challenge.” International Journal of
Computer Vision (2014): 1-42.
[13] Yi, Dong, et al. “Learning face representation from
scratch.” arXiv preprint arXiv:1411.7923 (2014).
[14] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E.
Hinton. “Imagenet classification with deep convolutional
ICSTSD 2016 | 1149
International Journal on Emerging Trends in Technology (IJETT)
ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456)
Volume 3 | Issue 2 | July - 2016 | Special Issue
www.ijett.in
neural networks.” Advances in neural information
processing systems. 2012.
[15] Chatfield, Ken, et al. “Return of the devil in the
details: Delving deep into convolutional nets.” arXiv
preprint arXiv:1405.3531 (2014).
[16] Hassner, Tal, et al. “Effective face frontalization in
unconstrained images.” arXiv preprint arXiv:1411.7964
(2014).
[17] Cheng-Ping Lee and Chun-Wen Chen,
"Classification of Age Groups Based on Facial Features"
Department of Computer Science and Information
Engineering Tamkang University Tamsui, Taipei, Taiwan
, R. O. C.
[18] "GENDER AND AGE ESTIMATION BASED ON
FACIAL IMAGES" TIN University of Computer
Studies, Yangon, Myanmar.
[19] "Consistency of Optimized Facial Features through
the Ages" Division of Computer Science and
Engineering, Center for Advanced Image and
Information Technology,Chonbuk National University
Jeonju, South Korea.
[20] "Age Estimation and Face Verification Across Aging
Using Landmarks" Tao Wu, Student Member, IEEE,
Pavan Turaga, Member, IEEE, and RamaChellappa,
Fellow, IEEE.
[21] Chin-Teng Lin1, Dong-Lin Li1,*, Jian-Hao Lai2,
Ming-Feng Han1 and Jyh-Yeong Chang "Automatic Age
Estimation System for Face Image"
[22] Xin Geng, Zhi-Hua Zhou, Yu Zhang, Gang Li,
Honghua Dai "Learning from Facial Aging Patterns for
Automatic Age Estimation" 1.School of Engineering and
Information Technology Deakin University, Victoria
, Australia, National Laboratory for Novel Software
Technology Nanjing University, Nanjing 210093, China.
[23] "Facial age estimation using BSIF and LBP"
CONFERENCE PAPER DECEMBER 2014.
[24] H. Bay, T. Tuytelaars, L. Van Gool, Surf: speeded up
robust features, in: Computer Vision–ECCV 2006,
Springer, 2006, pp. 404–417.
[25] K.-Y. Chang, C.-S. Chen, Y.-P. Hung, Ordinal
hyperplanes ranker with cost sensitivities for age
estimation, in: CVPR, IEEE, 2011, pp. 585–592.
[26] S.E. Choi, Y.J. Lee, S.J. Lee, K.R. Park, J. Kim, Age
estimation using a hierarchical classifier based on global
and local facial features, Pattern Recognit. 44 (6) (2011)
–1281.
[27] N. Dalal, B. Triggs, Histograms of oriented
gradients for human detection, in:CVPR, 1, IEEE, 2005,
pp. 886–893.
[28] C. Fernández, I. Huerta, A. Prati, A comparative
evaluation of regression learning algorithms for facial
age estimation, in: FFER in conjunction with ICPR, in
press, IEEE, 2014.
[29] A.C. Gallagher, T. Chen, Understanding images of
groups of people, in: IEEE Conference on Computer
Vision and Pattern Recognition, CVPR 2009., IEEE,
, pp. 256–263.
[30] X. Geng, C. Yin, Z.-H. Zhou, Facial age estimation
by learning from label distributions, in: TPAMI, vol. 35,
IEEE, 2013, pp. 2401–2412.
[31] X. Geng, Z.-H. Zhou, K. Smith-Miles, Automatic
age estimation based on facial aging patterns, TPAMI 29
(12) (2007) 2234–2240.
A. Gunay, V.V. Nabiyev, Automatic age classification
with lbp, in: 23rd International Symposium on Computer and
Information Sciences, 2008. ISCIS’08, IEEE, 2008, pp. 1–4.
Refbacks
- There are currently no refbacks.
Copyright © IJETT, International Journal on Emerging Trends in Technology