Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress in accuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.
KhiyariH.E. and WechslerH., Face recognition across time lapse using convolutional neural networks, J Inform Security7(3) (2016), 141–151.
2.
JainA.K., KlareB. and ParkU., Face matching and retrieval in forensics applications, IEEE Multimedia19(1) (2012), 1–9.
3.
JainA.K. and LiS.Z., Handbook of Face Recognition, SpringerNew York, (2011).
4.
ParkU., TongY. and JainA.K., Age-invariant face recognition, IEEE Trans Pattern Analysis Machine Intelligence (PAMI)32(5) (2010), 947–954.
5.
Face group, G.R.W., et al., FG-Net Aging Database, (2000).
6.
GengX., ZhouH-Z. and SmithK-M, Automatic age estimation based on facial aging patterns, IEEE Trans Pattern Analysi Machine Intelligence29(12) (2007), 2234–2240.
7.
FuY. and HuangT.S., Human age estimation with regression on discriminative aging manifold, IEEE Trans Multimedia10(4) (2008), 578–584.
8.
MuG., GuoG., FuY. and HuangT.S., Human age estimation using bio-inspired features, IEEE Conf Comput Vision Pattern Recognition (2009), 112–119.
9.
MontilloA. and LingH., Age regression from faces using random forests, IEEE Int Conf Image Processing (2009), 2465–2468.
10.
ZhuK., GongD., LiZ. and TangX., Orthogonal gaussian process for automatic age estimation, Int Conf Multimedia (2014), 857–860.
11.
LanitisA., TaylorC.J. and CootesT.F., Toward automatic simulation of aging effects on face images, IEEE Tran Pattern Analysis Machine Intelligence24(4) (2002), 442–455.
12.
SuoJ., ZhuS.C., ShanS. and ChenX., A compositional and dynamic model for face aging, IEEE Trans Pattern Analysis Machine Intelligence32(3) (2010), 385–401.
13.
SuoJ., ChenX., ShanS. and GaoW., Learning long term face aging patterns from partially dense aging databases, IEEE Conf Computer Vision (2009), 622–629.
14.
ShuX., TangJ., LaiH., LiuL. and YanS., Personalized age progression with aging dictionary, IEEE Conf Comput Vision (2015), 3970–3978.
15.
GongD., LiZ., TaoD., LiuJ. and LiX., A maximum entropy feature descriptor for age invariant face recognition, IEEE Conf Comput Vision Pattern Recognition (2015), 5289–5297.
16.
LiZ., ParkU. and JainA.K., A discriminative model for age invariant face recognition, IEEE Trans Inf Forensics Security6(3) (2011), 1028–1037.
17.
GongD., LiZ., LinD., LiuJ. and TangX., Hidden factor analysis for age invariant face recognition, IEEE Int Conf Comput Vision (ICCV) (2013), 2872–2879.
18.
ChenB., ChenC. and HsuW.H., Cross-age reference coding for age-invariant face recognition and retrieval, Euro Conf Comput Vision (2014), 768–783.
19.
LingH., SoattoS., RamanathanN. and JacobsD.W., Face verification across age progression using discriminative methods, IEEE Trans inform Forensics Security5(1) (2010), 82–91.
20.
OttoC., HaH. and JainA.K., How does aging affect facial components, Euro Conf Comput Vision Workshops Demonstrations (2012), 189–198.
21.
DuL. and LingH., Cross-age face verification by coordinating with cross-face age verification, IEEE Conf Comput vision Pattern Recognition (2015), 2329–2338.
22.
PrinceS.J. and ElderJ.H., Probabilistic linear discriminant analysis for inferences about identity, IEEE Conf on Computer Vision (2007), 1–8.
23.
IoffeS., Probabilistic linear discriminant analysis, IEEE Euro Conf Comput Vision (2006), 531–542.
24.
LiP., FuY., MohammedU., ElderJ.H. and PrinceS.J., Probabilistic models for inference about identity, IEEE Trans Pattern Analysis Machine Intelligence34(1) (2012), 144–157.
25.
ZhangY.J., Advances in Face Image Analysis: Techniques and Technologies, IGIGlobal (2010).
26.
DempsterA., LairdN. and RubinD., Maximum likelihood from incomplete data via the EM algorithm, J R Statistical Soc (1977), 1–38.
27.
RicanekK. and TesafayeT., Morph: A longitudinal image database of normal adult age-progression, IEEE Int Conf Automat Face Gesture Recognition2006, 341–345in.
ChenB., ChenC.S. and HsuW., Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset, IEEE Trans Multimedia17(6) (2015), 804–815.
30.
GuoG., FuY., DyerC. and HuangT.S., Image-based human age estimation by manifold learning and locally adjusted robust regression, IEEE Trans Image Process17(7) (2008), 1178–1188.
31.
CaiD., HeX., HanJ.W. and ZhangH., Orthogonal laplacian faces for face recognition, IEEE Trans Image Process15(11) (2006), 3608–3614.
32.
HardoonD.R., SzedmakS. and Shawe-TaylorJ., Canonical correlation analysis: an overview with application to learning methods, Neural Computing16(12) (2004), 2639–2664.
33.
AashmiS.S. and SaxenaS., Survey: Techniques for Aging Problems in Face Recognition, Int J Comput Sci Inform Techn4(2) (2014), 82–88.
34.
HunterD. and TiddemanB., Facial Ageing, Cambridge University Press, (2012).
35.
SuoJ., ChenX., ShanS., GaoW. and DaiQ., A Concatenational Graph Evolution Aging Model, IEEE Trans Pattern Analysis Machine Intelligence34(11) (2012), 2083–2096.
36.
LanitisA., TaylorC.J. and CootesT.F., Toward automatic simulation of aging effects on face images, IEEE Trans Pattern Analysis Machine Intelligence24(4) (2002), 442–455.
37.
OsmanA.A., SagayanV., SaeedA., AmeenH. and AzizA., Age-invariant face recognition system using combined shape and texture features, IET Biometrics4(2) (2015), 98–115.
38.
DeviN.S. and HemachandranK., Automatic Face Recognition System using Pattern Recognition Techniques: A Survey, Int J Comput Application83(5) (2013), 10–13.
39.
GongD., LiZ., LinD., LiuJ. and TangX., Hidden factor analysis for age invariant face recognition, IEEE Int Con f Comput Vision2013, 2872–2879in.
40.
ZhangZ., SongY. and QiH., Age Progression/Regression by Conditional Adversarial Autoencoder, IEEE Conf Comput Vision Pattern Recognition (2017), 5810–5818.
41.
AntipovG., BaccoucheM. and DugelayJ.L., Face Aging With Conditional Generative Adversarial Networks, IEEE Conf Image Processing (2017), 2089–2093.
42.
LingH., SoattoS., RamanathanN. and JacobsD.W., Face verification across age progression using discriminative methods, IEEE Trans Inform Forensics Security5(1) (2010), 82–91.
43.
LindebergT., Scale, Invariant, Feature, Transform, book Scholarpedia, Chapter:7(5) (2012), 10491–in.
44.
OjalaT., PietikainenM. and MaenpaaT., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans Pattern Analys Machine Intelligence24(7) (2002), 971–987.
45.
LiH., ZouH. and HuH., Modified hidden factor analysis for cross age face recognition, IEEE J Signal Processing Letters24(4) (2017), 465–469.
46.
ZhouH. and LamK.M., Age-invariant face recognition based on identity inference from appearance age, Pattern Recognition76 (2018), 191–202.
47.
YanC., LangC., WangT., DuX. and ZhangC., Age estimation based on convolutional neural network, Pacific Rim Conference on Multimedia2014, 211–220in.
48.
LiY., WangG., LinL. and ChangH., A deep joint learning approach for age invariant face verification, Vision2015, 296–305 in.
49.
XuC., LiuQ. and YeM., Age invariant face recognition and retrieval by coupled auto-encoder networks, Neurocomputing222 (2017), 62–71.
50.
LiY., WangG., NieL., WangQ. and TanW., Distance metric optimization driven convolutional neural network for age invariant face recognition, Pattern Recognition75 (2018), 51–62.
51.
ShakeelM.S. and LamK.M., Deep-feature encoding-based discriminative model for age-invariant face recognition, Pattern Recognition93 (2019), 442–457.
52.
AnandA., LabatiR.D., GenoveseA., BallesterE., PiuriV. and ScottiF., Age estimation based on face images and pretrained convolutional neural networks, IEEE Symp Series Comput Intelligence (SSCI), (2017), doi:10.1109/ssci.2017.8285381
53.
ParkhiO.M., VedaldiA. and ZissermanA., Deep face recognition, BMVC2015, 1–41in.
54.
WangY., GongD., ZhouZ., JiX., WangH., LiZ., LiuW. and ZhangT., Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition, European Conf Comput Vision (2018), 764–779.
55.
WangH., GongD., LiZ. and LiuW., Decorrelated adversarial learning for age-invariant face recognition, in: Proceedings, IEEE Conf Comput Vision Pattern Recognition2019, 3527–3536.
56.
CannyJ.F., A computational approach to edge detection, IEEE Trans pattern analysis mach intelligence6 (1986), 679–698.
57.
AyanzadehA., PourghaemiH. and SeyfariY., A modified ant colony based approach to digital image edge detection, IEEE Int Conf Knowledge-Based Engg Innov (KBEI), (2015).
58.
Irandoust-PakchinS., AyanzadehA. and BeikzadehS., Gaussian Three-Dimensional kernel SVM for Edge Detection Applications, ArXiv preprint arXiv:1710.01260 (2017).
59.
SharmaM., Dubey SujataR.B. and GuptaS.K., Feature Extraction of Mammograms, Int J Advan Comput Research2(3) (2012), 192–199.
60.
SirovichandL. and KirbyM., A low dimensional procedure for the characterization of human, Faces4(3) (1987), 519–524.
GongD., LiZ., TaoD., LiuJ. and LiX., A maximum entropy feature descriptor for age invariant face recognition, IEEE Conf Comput Vision Pattern Recognition (CVPR) (2015), 5289–5297.
63.
SawantM.M. and BhurchK.M., Age invariance face recognition: a survey on facial aging databases, techniques and effect of aging, Artificial Intell Review Springer (2018), 1–28.
64.
ChenB.-C., ChenC.-S. and HsuW.H., Face recognition and retrieval using cross-age reference coding with cross age celebrity dataset, IEEE Trans Multimedia17(6) (2015), 804–815.
65.
LiuZ., LuoP., WangX. and TangX., Deep learning face attributes in the wild, Int Conf Comput Vision (2015), 3730–3738.
66.
NechA. and ShlizermanI.K., Level playing field for million scale face recognition, IEEE Conf Comput Vision Pattern Recognition (2017), 7044–7053.
67.
IoffeS. and SzegedyC., Batch normalization: Accelerating deep network training by reducing internal covariate shift, Int Conf Machine Learning7 (2015), 448–456.
68.
ShlizermanI.K., SeitzS.M., MillerD. and BrossardE., The mega face benchmark: 1 million faces for recognition at scale, IEEE Conf Comput Vision Pattern Recognition (2016), 4873–4882.
69.
ZhouH., WongK. and LamK., Feature-aging for age-invariant face recognition, IEEE Asia-Pacific Signal Inform Processing Assoc Annual Summit and Conf (APSIPA) (2015), 1161–1165.
70.
EscaleraS., TorresM., MartinezB., BaroX., EscalanteH.J., GuyouI., Tz-imiropoulosG., CorneanuC., OliuM., BagheriM.A. and ValstarM., ChaLearn looking at people and faces of the world: face analysis workshop and challenge, IEEE Conf Comput Vision Pattern Recognition Workshop (2016), 1–8.
71.
RotheR., TimofteR. and GoolL.V., Dex: Deep expectation of apparent age from a single image, Int Conf Comput Vision Workshops (ICCVW) (2015), 252–257.
