Feature Based Techniques for a Face Recognition using Supervised Learning Algorithms based on Fixed Monocular Video Camera
Abstract
Abstract Views: 69
Automatic face recognition has ample significance in biometric research. Recent decades have witnessed enormous growth in this research area. Face-based identification is always considered more expedient as compared to other biometric authentications owing to its uniqueness and wide acceptance. The major contribution of this work is twofold; firstly, it comprises an extension of manual thresholding feature-based face recognition approach to an automatic feature-based supervised learning face recognition. Secondly, various new feature sets are proposed and tested on several classifiers for 2, 3, 4, and 5 persons. In addition, the use of slope features of facial components, such as the nose, right eye, left eye, and lips along with other conventional features for face recognition is also a unique contribution of this research. Multiple experiments were performed on the UMT face database. The results demonstrated a comparison of 5 different sets of feature-based approaches on 7 classifiers using the metrics of time efficiency and accuracy. They also depicted that the proposed approaches achieve a percentage accuracy of up to 95.5%.
Downloads
References
I. Anwar, S. Nawaz, G. Kibria, S. F. Ali, M. T. Hassan, and J.-B. Kim, “Feature based face recognition using slopes,” in 2014 Int. Conf. Cont., Automation Inform. Sci., IEEE, Gwangju, Korea (South), Dec. 2–5, 2014, pp. 200–205, doi: https://doi.org/10.1109/ICCAIS.2014.7020558
W. W. Bledsoe, “Man-machine facial recognition,” Rep. PRi, vol. 22, 1966.
S. B. Ahmed, S. F. Ali, J. Ahmad, M. Adnan, and M. M. Fraz, “On the frontiers of pose invariant face recognition: A review,” Artif. Intell. Rev., vol. 53, no. 4, pp. 2571–2634, Apr. 2020, doi: https://doi.org/10.1007/s10462-019-09742-3
C. Liu and H. Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition,” IEEE Trans. Image Process., vol. 11, no. 4, pp. 467–476, Apr. 2002, doi: https://doi.org/10.1109/TIP.2002.999679
L. Shen, L. Bai, and M. Fairhurst, “Gabor wavelets and general discriminant analysis for face identification and verification,” Image Vision Comput., vol. 25, no. 5, pp. 553– 563, May 2007, doi: https://doi.org/10.1016/j.imavis. 2006.05.002
S. Arca, P. Campadelli, and R. Lanzarotti, “A face recognition system based on local feature analysis,” in Int. Conf. Audio-and Video-Based Biomet. Person Authentica. Springer, 2003, pp. 182–189.
S. Zafeiriou, A. Tefas, and I. Pitas, “The discriminant elastic graph matching algorithm applied to frontal face verification,” Pattern Recognition, vol. 40, no. 6, pp. 2798–2810, 2007, doi: https://doi.org/10.1016/j.patcog. 2007.01.026
H. Shin, S.-D. Kim, and H.-C. Choi, “Generalized elastic graph matching for face recognition,” Pattern Recog. Lett., vol. 28, no. 7, pp. 1077– 1082, 2007, doi: https://doi.org/10.1016/j.patrec. 2007.01.003
A. Albiol, D. Monzo, A. Martin, J. Sastre, and A. Albiol, “Face recognition using hog–ebgm,” Pattern Recog. Lett., vol. 29, no. 8, pp. 1537–1543, July 2008, doi: https://doi.org/10.1016/j.patrec. 2008.03.017
M. Yang and L. Zhang, “Gabor feature based sparse representation for face recognition with gabor occlusion dictionary,” in Eur. Conf. Comput. Vision. Berlin, Heidelberg, Springer, 2010, pp. 448–461, doi: https://doi.org/10.1007/978-3- 642-15567-3_33
M. Yang, L. Zhang, S. C.-K. Shiu, and D. Zhang, “Monogenic binary coding: An efficient local feature extraction approach to face recognition,” IEEE Trans. Inform. Forens. Secur., vol. 7, no. 9, pp. 1738– 1751, Sep. 2012, doi: https://doi.org/10.1109/TIFS.20 12.2217332
Z. Chai, Z. Sun, H. Mendez- Vazquez, R. He, and T. Tan, “Gabor ordinal measures for face recognition,” IEEE Trans. Inform. Forens. Secur., vol. 9, no. 10, pp. 14–26, Nov. 2014, doi: https://doi.org/10.1109/TIFS.20 13.2290064 [13] R. J. Baron, “Mechanisms of human facial recognition,” Int. J. Man-Machine Stud., vol. 15, no. 13, pp. 137–178, Aug. 1981, doi: https://doi.org/10.1016/S0020- 7373(81)80001-6
S. Satonkar Suhas, B. Kurhe Ajay, and B. Prakash Khanale, “Face recognition using principal component analysis and linear discriminant analysis on holistic approach in facial images database,” Int. Organ. Sci. Res., vol. 2, no. 12, pp. 15–23, 2012.
L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” J. Opti. Soc. Am. A, vol. 4, no. 14, pp. 519–524, 1987, doi: https://doi.org/10.1364/JOSAA.4.000519
A. K. Jain and R. C. Dubes, Algorithms for clustering data. Prentice-Hall, Inc., 1988.
J. H. Lai, P. C. Yuen, and G. C. Feng, “Face recognition using holistic fourier invariant features,” Pattern Recog., vol. 34, no. 17, pp. 95– 109, 2001, doi: https://doi.org/10.1016/S0031-3203(99)00200-9
B.-L. Zhang, H. Zhang, and S. S. Ge, “Face recognition by applying wavelet subband representation and kernel associative memory,” IEEE Trans. Neural Netw., vol. 15, no. 19, pp. 166–177, 2004, doi: https://doi.org/10.1109/TNN.2003.820673
H. Zhang, B. Zhang, W. Huang, and Q. Tian, “Gabor wavelet associative memory for
face recognition,” IEEE Trans. Neural Netw., vol. 16, no. 20, pp. 275–278, 2005, doi: https://doi.org/10.1109/TNN.2004.841811
K.-C. Kwak and W. Pedrycz, “Face recognition using an enhanced independent component analysis approach,” IEEE Trans. Neural Netw, vol. 18, no. 21, pp. 530–541, Mar. 2007, doi: https://doi.org/10.1109/TNN.2006.885436
H. Huang and H. He, “Super-resolution method for face recognition using nonlinear mappings on coherent features,” IEEE Trans. Neural Netw., vol. 22, no. 22, pp. 121–130, Nov. 2011, doi: https://doi.org/10.1109/TNN.2010.2089470
R. Azad, B. Azad et al., “Optimized method for real-time face recognition system based on pca and multiclass support vector machine,” Adv. Comput. Sci., vol. 2, no. 23, pp. 126–132, 2013.
P. Zhang, X. Ben, W. Jiang, R. Yan, and Y. Zhang, “Coupled marginal discriminant mappings for low-resolution face recognition,” Optik-Int. J. Light Electron Optics, vol. 126, no. 24, pp. 4352–4357, Dec. 2015, doi:https://doi.org/10.1016/j.ijleo.2 015.08.138
Y. Li, Z. Mu, and T. Zhang, “Local feature extraction and recognition under expression variations based on multimodal face and ear spherical map,” 9th Int. Cong. Image Signal Process., BioMedical Eng. Informat. (CISP-BMEI), Datong, China, 15–17 Oct. 2016, pp. 286–290. https://doi.org/10.1109/CISP-BMEI.2016.7852723
Y. Chu, T. Ahmad, G. Bebis, and L. Zhao, “Low-resolution face recognition with single sample per person,” Signal Process., vol. 141, pp. 144–157, 2017, doi: https://doi.org/10.1016/j.sigpro. 2017.05.012
S. Aftab, S. F. Ali, A. Mahmood, and U. Suleman, “A boosting framework for human posture recognition using spatio-temporal features along with radon transform,” Multimed. Tools Appl., vol. 18, pp. 42325–42351, Aug. 2022, doi: https://doi.org/10.1007/s11042- 022-13536-1
S. F. Ali, R. Khan, A. Mahmood, M. T. Hassan, and M. Jeon, “Using temporal covariance of motion and geometric features via boosting for human fall detection,” Sensors, vol. 18, no. 6, Art. no. 1918, 2018, doi: https://doi.org/10.3390/s180619 18
M. Kirby and L. Sirovich, “Application of the karhunen-loeve procedure for the characterization of human faces,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 25, pp. 103–108, Jan. 1990, doi: https://doi.org/10.1109/34.4139 0
C. Sagonas, E. Ververas, Y. Panagakis, and S. Zafeiriou, “Recovering joint and individual components in facial data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 26, pp. 2668–2681, Dec. 2017, doi: https://doi.org/10.1109/TPAMI. 2017.2784421
W. Wang, Y. Yan, Z. Cui, J. Feng, S. Yan, and N. Sebe, “Recurrent face aging with hierarchical autoregressive memory,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 27, pp. 654–668, Feb. 2018, doi: https://doi.org/10.1109/TPAMI. 2018.2803166
P. Singhal, P. K. Srivastava, A. K. Tiwari, and R. K. Shukla, “A survey: Approaches to facial detection and recognition with machine learning techniques,”in Proc. Second Doct. Symp. Comput. Intell., vol. 40, no. 28, 2021, pp. 654–668, doi:
https://doi.org/10.1007/978-981-16-3346-1_9
X. Zhu, X. Liu, Z. Lei, and S. Z. Li, “Face alignment in full pose range: A 3d total solution,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. 41, no. 29, pp. 78–92, Nov. 2017, doi: https://doi.org/10.1109/TPAMI.2017.2778152
A. Mollahosseini, B. Hasani, M. J. Salvador, H. Abdollahi, D. Chan, and M. H. Mahoor, “Facial expression recognition from world wild web,” in Proc. IEEE Conf. Comput. Vision Pattern Recog. Workshops, vol. 42, no. 29, 2016, pp. 78–92.
M. Valstar, B. Martinez, X. Binefa, and M. Pantic, “Facial point detection using boosted regression and graph models,” in Comput. Vision Pattern Recog., 2010 IEEE Conf. IEEE, June 13–18, 2010, pp. 2729–2736, doi: https://doi.org/10.1109/CVPR.2010.5539996
S. F. Ali, A. S. Aslam, M. J. Awan, A. Yasin, and R. Damaˇseviˇcius, “Pose estimation of driver’s head panning based on interpolation and motion vectors under a boosting framework,” Appl.
Sci., vol. 11, no. 24, Art. no. 11600, Dec. 2021, doi: https://doi.org/10.3390/app112411600
S. F. Ali and M. T. Hassan, “Feature based techniques for a driver’s distraction detection using supervised learning algorithms based on fixed monocular video camera,” KSII Trans. Internet Info. Syst., vol. 12, no. 8, pp. 3820–3841, Aug. 2018.
S. F. Ali, M. Muaz, A. Fatima, F. Idrees, and N. Nazar, “Human fall detection,” in Inmic. IEEE, 2013, pp. 101–105.
H. Du, H. Shi, D. Zeng, X.-P. Zhang, and T. Mei, “The elements of end-to end deep face recognition: A survey of recent advances,” ACM Comput. Surveys (CSUR), vol. 54, no. 10s, pp. 1–42, 2022, doi: https://doi.org/10.1145/3507902
G. Jeevan, G. C. Zacharias, M. S. Nair, and J. Rajan, “An empirical study of the impact of masks on face recognition,” Pattern Recognition, vol. 122, Art. no. 108308, Feb. 2022, doi: https://doi.org/10.1016/j.patcog.2021.108308
M. Smith and S. Miller, “The ethical application of biometric facial recognition technology,” Ai & Society, vol. 37, no. 1, pp. 167–175, Apr. 2022, doi: https://doi.org/10.1007/s00146- 021-01199-9
Copyright (c) 2022 Sumaira Zafar, Isra Anwar, Jameel Ahmad, Malik Tahir Hassan, Syed Farooq Ali
This work is licensed under a Creative Commons Attribution 4.0 International License.
