Abstract

Three-dimensional (3D) face recognition has been a crucial task in human biometric verification and identification. A digital correlation method of a computer-generated hologram (CGH) for 3D face recognition is proposed, which encodes 3D data into a 2D hologram for recognition. The 3D face models are preprocessed and compressed to into groups of feature points. The CGH templates corresponding to the 3D feature points are generated by point- and layer-oriented algorithms based on three different numerical algorithms to encode depth values into 2D holograms. A 2D digital correlation is performed between the CGH templates. It is demonstrated that the generated CGHs templates could be effectively classified based on the correlation performance metrics of discrimination ratio, peak-to-correlation plane energy, and peak-to-noise ratio. With the essence of the CGH algorithm being the conversion of 3D data to a 2D hologram, the proposed encoding and decoding method has great advantages in reducing computational efforts and potential applications in 3D face recognition, storage, and display.

© 2019 Optical Society of America

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References

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2019 (2)

H. Zhou, U. Abeywickrema, B. Bordbar, L. Cao, and P. P. Banerjee, “Correlation of holograms for surface characterization of diffuse objects,” Proc. SPIE 10943, 1094306 (2019).
[Crossref]

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

2018 (1)

U. Abeywickrema, R. Gnawali, and P. P. Banerjee, “Identification of 3D objects using correlation of holograms,” Proc. SPIE 10752, 1075219 (2018).
[Crossref]

2017 (2)

U. Abeywickrema, P. Banerjee, A. Kota, S. Swiontek, and A. Lakhtakia, “High-resolution topograms of fingerprints using multiwavelength digital holography,” Opt. Eng. 56, 034117 (2017).
[Crossref]

H. Zhang, L. Cao, and G. Jin, “Computer-generated hologram with occlusion effect using layer-based processing,” Appl. Opt. 56, F138–F143 (2017).
[Crossref]

2016 (2)

2015 (2)

2014 (2)

G. Nehmetallah, P. P. Banerjee, M. Alam, and J. Khoury, “Performance evaluation of photorefractive two-beam coupling joint transform correlator,” Proc. SPIE 9094, 909409 (2014).
[Crossref]

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

2013 (1)

2012 (1)

Y. Zhao, L. Cao, H. Zhang, and Q. He, “Holographic display with LED illumination based on phase only spatial light modulator,” Proc. SPIE 8559, 85590B (2012).
[Crossref]

2009 (2)

2007 (1)

A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: a survey,” Pattern Recogn. Lett. 28, 1885–1906 (2007).
[Crossref]

2006 (1)

K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Comput. Vis. Image Underst. 101, 1–15 (2006).
[Crossref]

2005 (2)

E. Watanabe and K. Kodate, “Implementation of a high-speed face recognition system that uses an optical parallel correlator,” Appl. Opt. 44, 666–676 (2005).
[Crossref]

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

2003 (1)

V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1063–1074 (2003).
[Crossref]

2002 (1)

A. Awwal, K. Gudmundsson, M. Tabrez, M. Rahman, M. Alam, and K. Iftekharuddin, “A new metric for 3D optical pattern recognition,” Proc. SPIE 4788, 183–190 (2002).
[Crossref]

2000 (1)

1998 (1)

G. S. Pati, R. Tripathi, and K. Singh, “Photorefractive joint-transform correlator using incoherent-erasure in two-wave-mixing geometry,” Opt. Commun. 151, 268–272 (1998).
[Crossref]

1992 (1)

Abate, A. F.

A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: a survey,” Pattern Recogn. Lett. 28, 1885–1906 (2007).
[Crossref]

Abdel-Mottaleb, M.

A. Ansari and M. Abdel-Mottaleb, “3D face modeling using two views and a generic face model with application to 3D face recognition,” in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (2003), pp. 37–44.

Abeywickrema, U.

H. Zhou, U. Abeywickrema, B. Bordbar, L. Cao, and P. P. Banerjee, “Correlation of holograms for surface characterization of diffuse objects,” Proc. SPIE 10943, 1094306 (2019).
[Crossref]

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

U. Abeywickrema, R. Gnawali, and P. P. Banerjee, “Identification of 3D objects using correlation of holograms,” Proc. SPIE 10752, 1075219 (2018).
[Crossref]

U. Abeywickrema, P. Banerjee, A. Kota, S. Swiontek, and A. Lakhtakia, “High-resolution topograms of fingerprints using multiwavelength digital holography,” Opt. Eng. 56, 034117 (2017).
[Crossref]

Alam, M.

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

G. Nehmetallah, P. P. Banerjee, M. Alam, and J. Khoury, “Performance evaluation of photorefractive two-beam coupling joint transform correlator,” Proc. SPIE 9094, 909409 (2014).
[Crossref]

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

A. Awwal, K. Gudmundsson, M. Tabrez, M. Rahman, M. Alam, and K. Iftekharuddin, “A new metric for 3D optical pattern recognition,” Proc. SPIE 4788, 183–190 (2002).
[Crossref]

Alam, M. A.

Alam, M. S.

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

Ansari, A.

A. Ansari and M. Abdel-Mottaleb, “3D face modeling using two views and a generic face model with application to 3D face recognition,” in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (2003), pp. 37–44.

Awwal, A.

A. Awwal, K. Gudmundsson, M. Tabrez, M. Rahman, M. Alam, and K. Iftekharuddin, “A new metric for 3D optical pattern recognition,” Proc. SPIE 4788, 183–190 (2002).
[Crossref]

Bal, A.

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

Banerjee, P.

U. Abeywickrema, P. Banerjee, A. Kota, S. Swiontek, and A. Lakhtakia, “High-resolution topograms of fingerprints using multiwavelength digital holography,” Opt. Eng. 56, 034117 (2017).
[Crossref]

Banerjee, P. P.

H. Zhou, U. Abeywickrema, B. Bordbar, L. Cao, and P. P. Banerjee, “Correlation of holograms for surface characterization of diffuse objects,” Proc. SPIE 10943, 1094306 (2019).
[Crossref]

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

U. Abeywickrema, R. Gnawali, and P. P. Banerjee, “Identification of 3D objects using correlation of holograms,” Proc. SPIE 10752, 1075219 (2018).
[Crossref]

G. Nehmetallah, J. Khoury, M. A. Alam, and P. P. Banerjee, “Photorefractive two-beam coupling joint transform correlator: modeling and performance evaluation,” Appl. Opt. 55, 4011–4023 (2016).
[Crossref]

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

G. Nehmetallah, P. P. Banerjee, M. Alam, and J. Khoury, “Performance evaluation of photorefractive two-beam coupling joint transform correlator,” Proc. SPIE 9094, 909409 (2014).
[Crossref]

T.-C. Poon and P. P. Banerjee, Contemporary Optical Image Processing with MATLAB (Elsevier, 2001), Chap. 3, pp. 54–56.

P. P. Banerjee and T.-C. Poon, Principles of Applied Optics (Irwin, 1991).

H. Zhou, R. Hou, B. Bordbar, and P. P. Banerjee, “Effect of hologram windowing on correlation of 3D objects,” in Digital Holography and 3-D Imaging (OSA, 2019), paper Th2B.8.

Blanz, V.

V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1063–1074 (2003).
[Crossref]

Bordbar, B.

H. Zhou, U. Abeywickrema, B. Bordbar, L. Cao, and P. P. Banerjee, “Correlation of holograms for surface characterization of diffuse objects,” Proc. SPIE 10943, 1094306 (2019).
[Crossref]

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

H. Zhou, R. Hou, B. Bordbar, and P. P. Banerjee, “Effect of hologram windowing on correlation of 3D objects,” in Digital Holography and 3-D Imaging (OSA, 2019), paper Th2B.8.

Bowyer, K. W.

K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Comput. Vis. Image Underst. 101, 1–15 (2006).
[Crossref]

K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Face recognition using 2D and 3D facial data,” in Proceedings of ACM Workshop on Multimodal User Authentication (2003), pp. 25–32.

K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Multimodal 2D and 3D biometrics for face recognition,” in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (2003), pp. 187–194.

Cao, L.

H. Zhou, U. Abeywickrema, B. Bordbar, L. Cao, and P. P. Banerjee, “Correlation of holograms for surface characterization of diffuse objects,” Proc. SPIE 10943, 1094306 (2019).
[Crossref]

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

H. Zhang, L. Cao, and G. Jin, “Computer-generated hologram with occlusion effect using layer-based processing,” Appl. Opt. 56, F138–F143 (2017).
[Crossref]

Y. Zhao, L. Cao, H. Zhang, W. Tan, S. Wu, Z. Wang, Q. Yang, and G. Jin, “Time-division multiplexing holographic display using angular-spectrum layer-oriented method,” Chin. Opt. Lett. 14, 010005 (2016).
[Crossref]

Y. Zhao, L. Cao, H. Zhang, D. Kong, and G. Jin, “Accurate calculation of computer-generated holograms using angular-spectrum layer-oriented method,” Opt. Express 23, 25440–25449 (2015).
[Crossref]

H. Zhang, Y. Zhao, L. Cao, and G. Jin, “Fully computed holographic stereogram based algorithm for computer-generated holograms with accurate depth cues,” Opt. Express 23, 3901–3913 (2015).
[Crossref]

Y. Yi and L. Cao, “Optical fingerprint recognition based on local minutiae structure coding,” Opt. Express 21, 17108–17121 (2013).
[Crossref]

Y. Zhao, L. Cao, H. Zhang, and Q. He, “Holographic display with LED illumination based on phase only spatial light modulator,” Proc. SPIE 8559, 85590B (2012).
[Crossref]

Cao, X.

Y. Liu, X. Cao, Q. Dai, and W. Xu, “Continuous depth estimation for multi-view stereo,” in IEEE International Conference Computer Vision and Pattern Recognition (2009), pp. 2121–2128.

Chang, K.

K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Comput. Vis. Image Underst. 101, 1–15 (2006).
[Crossref]

Chang, K. I.

K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Face recognition using 2D and 3D facial data,” in Proceedings of ACM Workshop on Multimodal User Authentication (2003), pp. 25–32.

K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Multimodal 2D and 3D biometrics for face recognition,” in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (2003), pp. 187–194.

Dai, Q.

Y. Liu, X. Cao, Q. Dai, and W. Xu, “Continuous depth estimation for multi-view stereo,” in IEEE International Conference Computer Vision and Pattern Recognition (2009), pp. 2121–2128.

Donoghue, J.

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

Duda, R. O.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).

Durant, W. M.

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

Feng, J.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

Flynn, P.

K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Comput. Vis. Image Underst. 101, 1–15 (2006).
[Crossref]

Flynn, P. J.

K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Face recognition using 2D and 3D facial data,” in Proceedings of ACM Workshop on Multimodal User Authentication (2003), pp. 25–32.

K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Multimodal 2D and 3D biometrics for face recognition,” in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (2003), pp. 187–194.

Gnawali, R.

U. Abeywickrema, R. Gnawali, and P. P. Banerjee, “Identification of 3D objects using correlation of holograms,” Proc. SPIE 10752, 1075219 (2018).
[Crossref]

Goh, S.

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

Gudmundsson, K.

A. Awwal, K. Gudmundsson, M. Tabrez, M. Rahman, M. Alam, and K. Iftekharuddin, “A new metric for 3D optical pattern recognition,” Proc. SPIE 4788, 183–190 (2002).
[Crossref]

Hart, P. E.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).

He, L.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

He, Q.

Y. Zhao, L. Cao, H. Zhang, and Q. He, “Holographic display with LED illumination based on phase only spatial light modulator,” Proc. SPIE 8559, 85590B (2012).
[Crossref]

Horache, E.

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

Horner, J. L.

Hou, R.

H. Zhou, R. Hou, B. Bordbar, and P. P. Banerjee, “Effect of hologram windowing on correlation of 3D objects,” in Digital Holography and 3-D Imaging (OSA, 2019), paper Th2B.8.

Ichihashi, Y.

Iftekharuddin, K.

A. Awwal, K. Gudmundsson, M. Tabrez, M. Rahman, M. Alam, and K. Iftekharuddin, “A new metric for 3D optical pattern recognition,” Proc. SPIE 4788, 183–190 (2002).
[Crossref]

Ito, T.

Jiang, Z.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

Jin, G.

Jourabloo, A.

A. Jourabloo and X. Liu, “Large-pose face alignment via CNN-based dense 3D model fitting,” in IEEE International Conference on Computer Vision and Pattern Recognition (2016).

Khoury, J.

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

G. Nehmetallah, J. Khoury, M. A. Alam, and P. P. Banerjee, “Photorefractive two-beam coupling joint transform correlator: modeling and performance evaluation,” Appl. Opt. 55, 4011–4023 (2016).
[Crossref]

G. Nehmetallah, P. P. Banerjee, M. Alam, and J. Khoury, “Performance evaluation of photorefractive two-beam coupling joint transform correlator,” Proc. SPIE 9094, 909409 (2014).
[Crossref]

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

Kim, T.

Kodate, K.

Kong, D.

Kota, A.

U. Abeywickrema, P. Banerjee, A. Kota, S. Swiontek, and A. Lakhtakia, “High-resolution topograms of fingerprints using multiwavelength digital holography,” Opt. Eng. 56, 034117 (2017).
[Crossref]

Lakhtakia, A.

U. Abeywickrema, P. Banerjee, A. Kota, S. Swiontek, and A. Lakhtakia, “High-resolution topograms of fingerprints using multiwavelength digital holography,” Opt. Eng. 56, 034117 (2017).
[Crossref]

Liu, X.

A. Jourabloo and X. Liu, “Large-pose face alignment via CNN-based dense 3D model fitting,” in IEEE International Conference on Computer Vision and Pattern Recognition (2016).

Liu, Y.

Y. Liu, X. Cao, Q. Dai, and W. Xu, “Continuous depth estimation for multi-view stereo,” in IEEE International Conference Computer Vision and Pattern Recognition (2009), pp. 2121–2128.

Loo, C.

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

Luo, Y.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

Ma, Z.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

Martin, D. M.

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

Masuda, N.

Nakayama, H.

Nappi, M.

A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: a survey,” Pattern Recogn. Lett. 28, 1885–1906 (2007).
[Crossref]

Nehmetallah, G.

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

G. Nehmetallah, J. Khoury, M. A. Alam, and P. P. Banerjee, “Photorefractive two-beam coupling joint transform correlator: modeling and performance evaluation,” Appl. Opt. 55, 4011–4023 (2016).
[Crossref]

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

G. Nehmetallah, P. P. Banerjee, M. Alam, and J. Khoury, “Performance evaluation of photorefractive two-beam coupling joint transform correlator,” Proc. SPIE 9094, 909409 (2014).
[Crossref]

Niwa, M.

Pati, G. S.

G. S. Pati, R. Tripathi, and K. Singh, “Photorefractive joint-transform correlator using incoherent-erasure in two-wave-mixing geometry,” Opt. Commun. 151, 268–272 (1998).
[Crossref]

Peyghambarian, N.

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

Poon, T.-C.

T. Kim and T.-C. Poon, “Three-dimensional matching by use of phase only holographic information and the Wigner distribution,” J. Opt. Soc. Am. A 17, 2520–2528 (2000).
[Crossref]

P. P. Banerjee and T.-C. Poon, Principles of Applied Optics (Irwin, 1991).

T.-C. Poon and P. P. Banerjee, Contemporary Optical Image Processing with MATLAB (Elsevier, 2001), Chap. 3, pp. 54–56.

Quan, L.

C. Xu, Y. Wang, T. Tan, and L. Quan, “Depth vs. intensity: which is more important for face recognition?” in Proceedings of 17th International Conference on Pattern Recognition (2004), Vol. 4, pp. 342–345.

Rahman, M.

A. Awwal, K. Gudmundsson, M. Tabrez, M. Rahman, M. Alam, and K. Iftekharuddin, “A new metric for 3D optical pattern recognition,” Proc. SPIE 4788, 183–190 (2002).
[Crossref]

Regula, S.

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

Reingand, N.

O. Wolfgang and N. Reingand, Optical Imaging and Metrology: Advanced Technologies (Wiley, 2012).

Riccio, D.

A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: a survey,” Pattern Recogn. Lett. 28, 1885–1906 (2007).
[Crossref]

Sabatino, G.

A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: a survey,” Pattern Recogn. Lett. 28, 1885–1906 (2007).
[Crossref]

Schmidt, J. D.

J. D. Schmidt, Numerical Simulation of Optical Wave Propagation with Examples in MATLAB, Bellingham, Washington USA (SPIE, 2010).

Sharma, A.

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

Shimobaba, T.

Shiraki, A.

Singh, K.

G. S. Pati, R. Tripathi, and K. Singh, “Photorefractive joint-transform correlator using incoherent-erasure in two-wave-mixing geometry,” Opt. Commun. 151, 268–272 (1998).
[Crossref]

Stork, D. G.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).

Sugie, T.

Swiontek, S.

U. Abeywickrema, P. Banerjee, A. Kota, S. Swiontek, and A. Lakhtakia, “High-resolution topograms of fingerprints using multiwavelength digital holography,” Opt. Eng. 56, 034117 (2017).
[Crossref]

Tabrez, M.

A. Awwal, K. Gudmundsson, M. Tabrez, M. Rahman, M. Alam, and K. Iftekharuddin, “A new metric for 3D optical pattern recognition,” Proc. SPIE 4788, 183–190 (2002).
[Crossref]

Takada, N.

Tan, T.

C. Xu, Y. Wang, T. Tan, and L. Quan, “Depth vs. intensity: which is more important for face recognition?” in Proceedings of 17th International Conference on Pattern Recognition (2004), Vol. 4, pp. 342–345.

Tan, W.

Tripathi, R.

G. S. Pati, R. Tripathi, and K. Singh, “Photorefractive joint-transform correlator using incoherent-erasure in two-wave-mixing geometry,” Opt. Commun. 151, 268–272 (1998).
[Crossref]

Tu, X.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

Vetter, T.

V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1063–1074 (2003).
[Crossref]

Wang, Y.

C. Xu, Y. Wang, T. Tan, and L. Quan, “Depth vs. intensity: which is more important for face recognition?” in Proceedings of 17th International Conference on Pattern Recognition (2004), Vol. 4, pp. 342–345.

Wang, Z.

Watanabe, E.

Wolfgang, O.

O. Wolfgang and N. Reingand, Optical Imaging and Metrology: Advanced Technologies (Wiley, 2012).

Wu, S.

Xie, M.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

Xu, C.

C. Xu, Y. Wang, T. Tan, and L. Quan, “Depth vs. intensity: which is more important for face recognition?” in Proceedings of 17th International Conference on Pattern Recognition (2004), Vol. 4, pp. 342–345.

Xu, W.

Y. Liu, X. Cao, Q. Dai, and W. Xu, “Continuous depth estimation for multi-view stereo,” in IEEE International Conference Computer Vision and Pattern Recognition (2009), pp. 2121–2128.

Yamamoto, M.

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

Yang, Q.

Yi, Y.

Zhang, H.

Zhao, J.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

Zhao, Y.

Y. Zhao, L. Cao, H. Zhang, W. Tan, S. Wu, Z. Wang, Q. Yang, and G. Jin, “Time-division multiplexing holographic display using angular-spectrum layer-oriented method,” Chin. Opt. Lett. 14, 010005 (2016).
[Crossref]

Y. Zhao, L. Cao, H. Zhang, D. Kong, and G. Jin, “Accurate calculation of computer-generated holograms using angular-spectrum layer-oriented method,” Opt. Express 23, 25440–25449 (2015).
[Crossref]

H. Zhang, Y. Zhao, L. Cao, and G. Jin, “Fully computed holographic stereogram based algorithm for computer-generated holograms with accurate depth cues,” Opt. Express 23, 3901–3913 (2015).
[Crossref]

Y. Zhao, L. Cao, H. Zhang, and Q. He, “Holographic display with LED illumination based on phase only spatial light modulator,” Proc. SPIE 8559, 85590B (2012).
[Crossref]

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

Zhou, H.

H. Zhou, U. Abeywickrema, B. Bordbar, L. Cao, and P. P. Banerjee, “Correlation of holograms for surface characterization of diffuse objects,” Proc. SPIE 10943, 1094306 (2019).
[Crossref]

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

H. Zhou, R. Hou, B. Bordbar, and P. P. Banerjee, “Effect of hologram windowing on correlation of 3D objects,” in Digital Holography and 3-D Imaging (OSA, 2019), paper Th2B.8.

Appl. Opt. (4)

Chin. Opt. Lett. (1)

Comput. Vis. Image Underst. (1)

K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Comput. Vis. Image Underst. 101, 1–15 (2006).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1063–1074 (2003).
[Crossref]

J. Opt. Soc. Am. A (1)

Opt. Commun. (1)

G. S. Pati, R. Tripathi, and K. Singh, “Photorefractive joint-transform correlator using incoherent-erasure in two-wave-mixing geometry,” Opt. Commun. 151, 268–272 (1998).
[Crossref]

Opt. Eng. (2)

U. Abeywickrema, P. Banerjee, A. Kota, S. Swiontek, and A. Lakhtakia, “High-resolution topograms of fingerprints using multiwavelength digital holography,” Opt. Eng. 56, 034117 (2017).
[Crossref]

M. Alam, A. Bal, E. Horache, S. Goh, C. Loo, S. Regula, and A. Sharma, “Metrics for evaluating the performance of joint transform correlation based target recognition and tracking algorithms,” Opt. Eng. 44, 067005 (2005).
[Crossref]

Opt. Express (5)

Pattern Recogn. Lett. (1)

A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: a survey,” Pattern Recogn. Lett. 28, 1885–1906 (2007).
[Crossref]

Proc. SPIE (7)

A. Awwal, K. Gudmundsson, M. Tabrez, M. Rahman, M. Alam, and K. Iftekharuddin, “A new metric for 3D optical pattern recognition,” Proc. SPIE 4788, 183–190 (2002).
[Crossref]

P. P. Banerjee, U. Abeywickrema, H. Zhou, B. Bordbar, M. Alam, G. Nehmetallah, J. Khoury, and L. Cao, “Taking correlation from 2D to 3D: Optical methods and performance evaluation,” Proc. SPIE 10995, 109950B (2019).
[Crossref]

G. Nehmetallah, P. P. Banerjee, M. Alam, and J. Khoury, “Performance evaluation of photorefractive two-beam coupling joint transform correlator,” Proc. SPIE 9094, 909409 (2014).
[Crossref]

J. Khoury, M. S. Alam, P. P. Banerjee, G. Nehmetallah, W. M. Durant, D. M. Martin, J. Donoghue, N. Peyghambarian, and M. Yamamoto, “Performance comparison of photorefractive two-beam coupling correlator with optimal filter based correlators,” Proc. SPIE 9094, 909405 (2014).
[Crossref]

U. Abeywickrema, R. Gnawali, and P. P. Banerjee, “Identification of 3D objects using correlation of holograms,” Proc. SPIE 10752, 1075219 (2018).
[Crossref]

H. Zhou, U. Abeywickrema, B. Bordbar, L. Cao, and P. P. Banerjee, “Correlation of holograms for surface characterization of diffuse objects,” Proc. SPIE 10943, 1094306 (2019).
[Crossref]

Y. Zhao, L. Cao, H. Zhang, and Q. He, “Holographic display with LED illumination based on phase only spatial light modulator,” Proc. SPIE 8559, 85590B (2012).
[Crossref]

Other (14)

O. Wolfgang and N. Reingand, Optical Imaging and Metrology: Advanced Technologies (Wiley, 2012).

P. P. Banerjee and T.-C. Poon, Principles of Applied Optics (Irwin, 1991).

H. Zhou, R. Hou, B. Bordbar, and P. P. Banerjee, “Effect of hologram windowing on correlation of 3D objects,” in Digital Holography and 3-D Imaging (OSA, 2019), paper Th2B.8.

J. D. Schmidt, Numerical Simulation of Optical Wave Propagation with Examples in MATLAB, Bellingham, Washington USA (SPIE, 2010).

H. Stark, ed., Applications of Optical Transforms (Academic, 1982).

T.-C. Poon and P. P. Banerjee, Contemporary Optical Image Processing with MATLAB (Elsevier, 2001), Chap. 3, pp. 54–56.

Y. Liu, X. Cao, Q. Dai, and W. Xu, “Continuous depth estimation for multi-view stereo,” in IEEE International Conference Computer Vision and Pattern Recognition (2009), pp. 2121–2128.

C. Xu, Y. Wang, T. Tan, and L. Quan, “Depth vs. intensity: which is more important for face recognition?” in Proceedings of 17th International Conference on Pattern Recognition (2004), Vol. 4, pp. 342–345.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley, 2001).

A. Ansari and M. Abdel-Mottaleb, “3D face modeling using two views and a generic face model with application to 3D face recognition,” in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (2003), pp. 37–44.

K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Face recognition using 2D and 3D facial data,” in Proceedings of ACM Workshop on Multimodal User Authentication (2003), pp. 25–32.

K. I. Chang, K. W. Bowyer, and P. J. Flynn, “Multimodal 2D and 3D biometrics for face recognition,” in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (2003), pp. 187–194.

X. Tu, J. Zhao, Z. Jiang, Y. Luo, M. Xie, Y. Zhao, L. He, Z. Ma, and J. Feng, “Joint 3D face reconstruction and dense face alignment from a single image with 2D-assisted self-supervised learning,” in IEEE International Conference Computer Vision and Pattern Recognition (2019).

A. Jourabloo and X. Liu, “Large-pose face alignment via CNN-based dense 3D model fitting,” in IEEE International Conference on Computer Vision and Pattern Recognition (2016).

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Figures (12)

Fig. 1.
Fig. 1. Schematic diagram of a preprocessed face model. The red spheres labeled from 1 to 32 represent the feature points of the face. Points 1–10 are the contour of the face; points 11–22 give the area of two eyes; points 23–26 represent the nose bridge; and points 27–32 are six points on the contour of the mouth.
Fig. 2.
Fig. 2. Diagram of the point-oriented and layer-oriented method to calculate phase holograms.
Fig. 3.
Fig. 3. Schematic diagram of point-oriented propagation coordinates.
Fig. 4.
Fig. 4. Five typical face models and corresponding holograms. Face 1 and Face 6 are images of the same person with different facial expressions. Faces 9, 11, and 12 are the faces of different individuals.
Fig. 5.
Fig. 5. (a) CGH generated by the Rayleigh–Sommerfeld point-oriented algorithm with $d=500\,\,{\rm mm}$ ; (b) CGH generated by the angular spectrum layer-oriented algorithm with $d=150\,\,{\rm mm}$ ; (c) CGH generated by the Fresnel diffraction point-oriented algorithm with $d=1000\,\,{\rm mm}$ ; and (d) Typical correlation result for two CGH correlations. The correlation peak value is labeled as $7.3\times {10^4}$ .
Fig. 6.
Fig. 6. Correlation peak values for the verification of human faces. The dashed magenta line is a threshold for distinguishing the same face as the reference or not, which is equal to $4\times {10^5}$ . (a) 32 feature points and (b) 28 feature points.
Fig. 7.
Fig. 7. (a) CGHs for 20 faces by the angular spectrum algorithm, mapping in $4\times 5$ cells. (b) Correlation coefficients for correlation between Figs. 5(a) and 7(a). Autocorrelation peak is labeled as Peak 1 and the correlation peak between Face 1 and Face 6 is labeled as Peak 2.
Fig. 8.
Fig. 8. Correlation peak values for Faces 1–20 sequentially being set as a reference. A dashed cyan line is set as a threshold.
Fig. 9.
Fig. 9. (a) DR results for Fig. 8(a). A dashed cyan line with a value of 3 is drawn to separate PCE values into two groups to evaluate whether the target face and reference face are obtained from the same person. Three colors and symbols denote three CGH algorithms. (b) Boxplot for (a). Black solid lines are the maximum and minimum values; blue horizontal lines are the first and third quartiles labeled as ${Q}1$ and ${Q}3$ ; red solid line is the median, labeled as ${Q}2$ ; the red cross are the values smaller than ${Q_1}-1.5\times ({{Q_2}-{Q_1}})$ or larger than ${Q_3}+1.5\times ({{Q_3}-{Q_2}})$ ; and the blue circle includes the values beneath the dashed cyan line in (a).
Fig. 10.
Fig. 10. (a)  ${{\log}_{10}}$ PCE results for Fig. 8(a). (b) Boxplot for (a), showing a statistic analysis of ${{\log}_{10}}$ PCE.
Fig. 11.
Fig. 11. (a)  ${{\log}_{10}}$ PNR results for Fig. 8(a) . (b) Boxplot for (a), showing a statistic analysis of ${{\log}_{10}}$ PNR.
Fig. 12.
Fig. 12. (a) Correlation peak values for the recognition of human faces with $d=150\,\,{\rm mm}$ for Rayleigh–Sommerfeld, and the angular spectrum, $d=1000\,\,{\rm mm}$ for Rayleigh–Sommerfeld and Fresnel diffraction. (b) DR results for (a).

Tables (1)

Tables Icon

Table 1. Summary of Performance Metrics for Correlation

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

φ ( x , y ) = arctan ( I m ( h ( x , y ) ) R e ( h ( x , y ) ) ) ,
h ( x , y ) = j k 0 2 π i = 1 32 A i r i exp [ j ( k 0 r i + ϕ i ) ] ,
r i = ( x i x ) 2 + ( y i y ) 2 + ( d z i ) 2 .
h ( x , y ) = i = 1 l F 1 { H p i F [ A i ( x , y ) exp ( j ϕ i ( x , y ) ) ] } ,
H p i ( k x , k y ) = exp [ j k 0 ( d z i ) 1 ( k x x ) 2 ( k y y ) 2 ] .
r i ( d z i ) [ 1 + 1 2 ( x i x d z i ) 2 + 1 2 ( y i y d z i ) 2 ] .
h ( x , y ) = j k 0 2 π i = 1 32 A i exp [ j k 0 ( d z i ) ] ( d z i ) × exp [ j ( k 0 2 ( d z i ) ( ( x i x ) 2 + ( y i y ) 2 ) + ϕ i ) ] .
d m a x { M Δ x 4 ( Δ x ) 2 λ 2 1 ,   N Δ y 4 ( Δ y ) 2 λ 2 1 } .
d 3 π 4 λ ( L 0 2 + W 0 2 + L 2 + W 2 ) 2 ,

Metrics