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Evaluation of Two Feature Extraction Techniques for Age-Invariant Face Recognition

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Computational Methods and Data Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1227))

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Abstract

Huge variation in facial appearances of the same individual makes Age-Invariance Face Recognition (AIFR) task suffer from the misclassification of faces. However, some Age-Invariant Feature Extraction Techniques (AI-FET) for AIFR are emerging to achieve good recognition results. The performance results of these AI-FETs need to be further investigated statistically to avoid being misled. Here, the means between the quantitative results of Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA) and Histogram of Gradient (HoG) are compared using one-way Analysis of Variance (ANOVA). The ANOVA results obtained at 0.05 critical significance level indicate that the results of the HoG and PCA-LDA techniques are statistically well in line because the F-critical value was found to be greater than the value of the calculated F-statistics in all the calculations.

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References

  1. Shu C, Ding X, Fang C (2011) Histogram of oriented gradient (HOG) of the oriented gradient for face recognition. Tsinghai Sci Technol 16(2):216–224

    Article  Google Scholar 

  2. Zhifeng L, Dihong G, Xuelong L, Dacheng T (2016) Aging face recognition: a hierarchical learning model based on local patterns selection. IEEE Trans Image Process 25(5):2146–2154

    Article  MathSciNet  MATH  Google Scholar 

  3. Dihong G, Zhifeng L, Dahua L, Jianzhuang L, Xiaoou T (2013) Hidden factor analysis for age invariant Face Recognition. In: Proceedings of the IEEE international conference on computer vision, pp 2872–2879

    Google Scholar 

  4. Dihong G, Zhifeng L, Dacheng T, Jianzhuang L, Xuelong L (2015) A maximum entropy feature descriptor for age-invariant Face Recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5289–5297

    Google Scholar 

  5. Zhifeng L, Unsang P, Anil KJ (2011) A discriminative model for age invariant face recognition. IEEE Trans Inf Forensics Secur 6(3):1028–1037

    Article  Google Scholar 

  6. Haibin L, Stefano S, Narayanan R, David WJ (2010) Face verification across age progression using discriminative methods. IEEE Trans Inf Forensics Secur 5(1):82–91

    Article  Google Scholar 

  7. Chenfei X, Qihe L, Mao Y (2017) Age invariant FR and retrieval by coupled auto-encoder networks. Neurocomputing 222:62–71

    Article  Google Scholar 

  8. Di H, Mohsen A, Yunhong W, Liming C (2012) 3-D FR using e LBP-based facial description and local feature hybrid matching. IEEE Trans Inf Forensics Secur 7(5):1551–1565

    Article  Google Scholar 

  9. Yulan G, Yinjie L, Li L, Yan W, Mohammed B, Ferdous S (2016) EI3D: Expression-invariant 3D FR based on feature and shape matching. Pattern Recogn Lett 83:403–412

    Article  Google Scholar 

  10. Stefano B, Naoufel W, Albertodel B, Pietro P (2013) Matching 3D face scans using interest points and local histogram descriptors. Comput Graph 37(5):509–525

    Google Scholar 

  11. Stefano B, Naoufel W, Alberto B, Pietro P (2014) Selecting stable key points and local descriptors for person identification using 3D face scans. Vis Comput 30(11):1275–1292

    Google Scholar 

  12. Alexander MB, Michael MB, Ron K (2007) Expression-invariant representations of faces. IEEE Trans Image Process 16(1):188–197

    Article  MathSciNet  Google Scholar 

  13. Di H, Caifeng S, Mohsen A, Yunhong W, Liming C (2011) Local binary patterns and its application to facial image analysis. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):765–781

    Article  Google Scholar 

  14. Kavitha J, Mirnalinee TT (2016) Automatic frontal face reconstruction approach for pose invariant face recognition. Procedia Comput Sci 87:300–305

    Article  Google Scholar 

  15. Goesta HG (1978) In search of a general picture processing operator. Comput Graph Image Process 8(2):155–173

    Google Scholar 

  16. Navneet D, Bill T (2005) Histograms of oriented gradients for human detection. In: IEEE conference on computer vision and pattern recognition (CVPR). San Diego, CA, USA, pp 886–893

    Google Scholar 

  17. Liu CL, Nakashima K, Sako H (2004) Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recogn 37(2):265–279

    Google Scholar 

  18. Liu H (2006) Offline handwritten character recognition based on descriptive model and discriminative learning [Dissertation]. Tsinghua University, Beijing, China

    Google Scholar 

  19. Lawrence S, Kirby M (1987) A low dimensional procedure for the characterization of human face. JOSA 4(3):519–524

    Google Scholar 

  20. Peter NB, Joao PH, David JK (1977) Eigen faces vs. fisher faces: recognition using class specific linear projection. IEEE Trans Patt Anal Mach Intell 9(7):711–720

    Google Scholar 

  21. Ravi S, Nayeem S (2013) A study on face recognition technique based on eigen face. Int J Appl Inf Syst 5(4):57–62

    Google Scholar 

  22. Sakai T, Nagao M, Fujibayashi S (1969) Line extraction and pattern recognition in a photograph. Pattern Recogn 1:233–248

    Article  Google Scholar 

  23. Nikolaos G, Vasileios M, Ioannis K, Tania S (2013) Mixture subclass discriminant analysis link to restricted Gausian model and other generalizations. IEEE Trans Neur Netw Learn Syst 24(1):8–21

    Google Scholar 

  24. Dihong G, Zhifeng L, Dahua L, Jianzhuang L, Xiaoou T (2013) Hidden factor analysis for age invariant face recognition. In: IEEE international conference on computer vision, pp 2872–2879

    Google Scholar 

  25. Priti VS, Bl G (2012) particle swarm optimization—best feature selection method for face images. Int J Sci Eng Res 3(8):1–5

    Google Scholar 

  26. Issam D (2008) Quadratic kernel-free non-linear support vector machine. Springer J Glob Optim 41(1):15–30

    Article  MathSciNet  MATH  Google Scholar 

  27. Ayodele O, Temitayo MF, Stephen O, Elijah O, John O (2017) Statistical evaluation of emerging feature extraction techniques for aging-invariant face recognition systems. FUOYE J Eng Technol 2(1):129–134

    Google Scholar 

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Authors and Affiliations

  1. SRM University, Delhi NCR, Sonepat, Haryana, India

    Ashutosh Dhamija & R. B. Dubey

Authors
  1. Ashutosh Dhamija
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  2. R. B. Dubey
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Corresponding author

Correspondence to Ashutosh Dhamija .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Vijendra Singh

  2. Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH, USA

    Vijayan K. Asari

  3. Department of Computer Science and Engineering, SRM University Delhi-NCR, Sonepat, Haryana, India

    Sanjay Kumar

  4. Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, Punjab, India

    R. B. Patel

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Dhamija, A., Dubey, R.B. (2021). Evaluation of Two Feature Extraction Techniques for Age-Invariant Face Recognition. In: Singh, V., Asari, V., Kumar, S., Patel, R. (eds) Computational Methods and Data Engineering. Advances in Intelligent Systems and Computing, vol 1227. Springer, Singapore. https://doi.org/10.1007/978-981-15-6876-3_15

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