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A Novel Approach to Mugshot Based Arbitrary View Face Recognition
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 Title & Authors
A Novel Approach to Mugshot Based Arbitrary View Face Recognition
Zeng, Dan; Long, Shuqin; Li, Jing; Zhao, Qijun;
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 Abstract
Mugshot face images, routinely collected by police, usually contain both frontal and profile views. Existing automated face recognition methods exploited mugshot databases by enlarging the gallery with synthetic multi-view face images generated from the mugshot face images. This paper, instead, proposes to match the query arbitrary view face image directly to the enrolled frontal and profile face images. During matching, the 3D face shape model reconstructed from the mugshot face images is used to establish corresponding semantic parts between query and gallery face images, based on which comparison is done. The final recognition result is obtained by fusing the matching results with frontal and profile face images. Compared with previous methods, the proposed method better utilizes mugshot databases without using synthetic face images that may have artifacts. Its effectiveness has been demonstrated on the Color FERET and CMU PIE databases.
 Keywords
Mugshot-based face recognition;Arbitrary view face recognition;Three-dimensional face reconstruction;
 Language
English
 Cited by
 References
1.
C. Ding and D. Tao, “A comprehensive survey on poseinvariant face recognition,” arXiv preprint arXiv:1502.04383 (2015).

2.
X. Zhang and Y. Gao, “Face recognition across pose: A review,” Pattern Recognition 42, 2876-2896 (2009). crossref(new window)

3.
K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition,” Computer Vision and Image Understanding 101, 1-15 (2006). crossref(new window)

4.
X. Chai, S. Shan, X. Chen, and W. Gao, “Locally linear regression for pose-invariant face recognition,” IEEE Transactions on Image Processing 16, 1716-1725 (2007). crossref(new window)

5.
A. Astnana, T. K. Marks, M. J. Jones, K. H. Tieu, and M. Rohith, “Fully automatic pose-invariant face recognition via 3D pose normalization,” in Proc. IEEE International Conference on Computer Vision (Barcelona, Spain, Nov. 2011), pp. 937-944.

6.
X. Shao, X. Zhou, C. Cheng, and T. X. Han, “3D face reconstruction and dynamic feature extraction for poseinvariant face recognition,” in Proc. International Symposium on Computer, Communication, Control and Automation (Singapore, Singapore, Dec. 2013), pp. 119-122.

7.
A. Franco, D. Miao, and D. Maltoni, “2D face recognition based on supervised subspace learning from 3D models,” Pattern Recognition 41, 3822-3833 (2008). crossref(new window)

8.
J. Heo and M. Savvides, “Gender and ethnicity specific generic elastic models from a single 2D image for novel 2D pose face synthesis and recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 2341-2350 (2012). crossref(new window)

9.
A. Li, S. Shan, X. Chen, and W. Gao, “Maximizing intraindividual correlations for face recognition across pose differences,” in Proc. IEEE International Conference on Computer Vision and Pattern Recognition (Miami Beach, Florida, USA, Jun. 2009), pp. 605-611.

10.
D. Yi, Z. Lei, and S. Z. Li, “Towards pose robust face recognition,” in Proc. IEEE International Conference on Computer Vision and Pattern Recognition (Portland, Oregon, USA, Jun. 2013), pp. 3539-3545.

11.
X. Zhang, Y. Gao, and M. K. H. Leung, “Recognizing rotated faces from frontal and side views: An approach toward effective use of mugshot databases,” IEEE Transactions on Information Forensics and Security 3, 684-697 (2008). crossref(new window)

12.
H. Han and A. K. Jain, “3D face texture modeling from uncalibrated frontal and profile images,” in Proc. IEEE International Conference on Biometrics: Theory, Applications and Systems (Doubletree Hotel, Crystal City, USA, Sept. 2012), pp. 223-230.

13.
P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, “The FERET evaluation methodology for face-recognition algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1090-1104 (2000). crossref(new window)

14.
T. Sim, S. Baker, and M. Bsat, “The CMU pose, illumination, and expression database,” in Proc. IEEE International Conference on Automatic Face and Gesture Recognition (Washington D.C., USA, May 2002), pp. 46-51.

15.
J. Li, S. Long, D. Zeng, and Q. Zhao, “Example-based 3D face reconstruction from uncalibrated frontal and profile images,” in Proc. IEEE International Conference on Biometrics (Phuket, Thailand, May 2015), pp. 193-200.

16.
K. Bonnen, B. F. Klare, and A. K. Jain, “Component-based representation in automated face recognition,” IEEE Transactions on Information Forensics and Security 8, 239-253 (2013). crossref(new window)

17.
T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: Application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 2037-2041 (2006). crossref(new window)

18.
NeuroTechnology, http://www.neurotechnology.com.