Hybrid Facial Features with Application in Person Identification

Authors

  • Boonchana Purahong Department of Computer Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
  • Vanvisa Chutchavong Department of Computer Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
  • Hisayuki Aoyama Department of Mechanical and Intelligent Systems Engineering, University of Electro-Communications, Japan
  • Chuchart Pintavirooj Departmet of Biomedical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand

DOI:

https://doi.org/10.13052/jmm1550-4646.161212

Keywords:

Geometric invariance, person identification, face identification, hybrid facial feature, facial image.

Abstract

This paper presents the hybrid facial feature with identification and verification based on facial images. A query facial image had been taken under different conditions of the facial image of the same person (as the query). The query facial image database was constructed. We have used the technique of three-dimensional (3D) Dlib facial landmarks using a direct linear transform technique. A set of absolute affine invariance had been constructed from a series of the 3D landmark quadruplets, which make the facial identification robust to affine geometric transformation. These 3D facial features serve as a coarse feature depending on each individual facial structure. The construct of the 2D detail features represents the edge facial image confined between the 2D Dlib landmarks. The similarity of the 2D feature is achieved by aligning the 2D query edge image against that of the reference edge image. The geometric transformation matrix is estimated from the 2D Dlib landmarks, where correspondence is well established. An identification/verification cost function using a combination of local 2D facial features and global 3D facial features is utilized to verify and identify a query facial image against a candidate facial image(s). The performance of the algorithm yielding an area of 99.97% perfect classification is represented as a value under the receiver operating characteristic (ROC) curve.  

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Author Biographies

Boonchana Purahong, Department of Computer Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand

Boonchana Purahong received his M.Eng. degree in information engineering from the King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand. He has joined the department of computer engineering, faculty of engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand. He is currently an associate professor and interested in pattern recognition and image processing.

Vanvisa Chutchavong, Department of Computer Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand

Vanvisa Chutchavong received her Ph.D. degree in electrical engineering from the King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand. She has joined the department of computer engineering, faculty of engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand. She is currently an associate professor and interested in the filter circuit.

Hisayuki Aoyama, Department of Mechanical and Intelligent Systems Engineering, University of Electro-Communications, Japan

Hisayuki Aoyama received his Ph.D. degree in informatics and engineering from the Tokyo Institute of Technology, Tokyo, Japan. Currently, he is working as a professor in the Department of Mechanical Engineering and Intelligent Systems, Graduate School of Informatics and Engineering. He is interested in micro/precision engineering and micrometrology as well as its industrial and biomedical applications.

Chuchart Pintavirooj, Departmet of Biomedical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand

Chuchart Pintavirooj received his B.Sc. and M.Sc. degrees from the Mahidol University, Bangkok, Thailand in 1985 and 1989, respectively. In 1995, he received another master’s degree in biomedical engineering from the Worcester Polytechnic Institute, MA, USA. In 2000, he earned a Ph.D. degree in biomedical engineering from the Drexel University, Philadelphia, USA. He joined the Department of Biomedical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand. He is currently an associate professor. His research interests include image reconstruction, image classification, and image restoration.

References

G. Shalini, M. Mia, and B. Alan. Anthropometric 3D Face Recognition. International Journal of Computer Vision, 90:331-349, 2010.

S.Z. Li and A.K. Jain. Handbook of Face Recognition, Springer, 2011.

A. Andrea, N. Michele, R. Daniel, and S. Gabriele. 2D and 3D Face Recognition: A Survey. Pattern Recognition Letters, 28:1885-1906, 2007.

V.C. Kagawade and S.A. Angadi. Multi-directional local gradient descriptor: A new feature descriptor for face recognition, Image and Vision Computing, 83:39-50, 2019.

B.P. Rinky, P. Mondal, K. Manikantan, and S. Ramachandran. DWT based Feature Extraction using Edge Tracked Scale Normalization for Enhanced Face Recognition, Procedia Technology, 6:344-353, 2012.

A. Juhong and C. Pintavirooj. Face recognition based on facial landmark detection, Biomedical Engineering International Conference, 1-4, 2017.

T. Özseven and M. Düğenci. Face recognition by distance and slope between facial landmarks, International Artificial Intelligence and Data Processing Symposium, 1-4, 2017.

Y. Martínez-Díaz, N. Hernández, R.J. Biscay, L. Chang, H. Méndez-Vázquez, and L. E. Sucar. On Fisher vector encoding of binary features for video face recognition, Journal of Visual Communication and Image Representation, 51:155-161, 2018.

Z. Wang and X. Sun. Multiple kernel local Fisher discriminant analysis for face recognition, Signal Processing, 93:1496-1509, 2013.

E. Vezzetti, F. Marcolin, and G. Fracastoro. 3D face recognition: An automatic strategy based on geometrical descriptors and landmarks, Robotics and Autonomous Systems, 62:1768-1776 ,2014.

H. Drira, B. Ben Amor, A. Srivastava, M. Daoudi and R. Slama. 3D Face Recognition under Expressions Occlusions, and Pose Variations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35:2270-2283, 2013.

A. B. Moreno, A. Sanchez, J. Velez and J. Diaz. Face recognition using 3D local geometrical features: PCA vs. SVM, International Symposium on Image and Signal Processing and Analysis, 185-190, 2005.

S. Soltanpour, B. Boufama, and Q.M. Jonathan Wu. A survey of local feature methods for 3D face recognition, Pattern Recognition, 72:391-406, 2017.

S. Anping, X. Guoliang, D. Xuehai, S. Jiaxin, X. Gang, Z. Wu, Assessment for facial nerve paralysis based on facial asymmetry, Phys. Eng. Sci. Med, 40(4): 851–860, 2017.

A. Tabatabaei Balaei, K. Sutherland, P. Cistulli, and P. de Chazal. Automatic detection of obstructive sleep apnea using facial images, International Symposium on Biomedical Imaging, 215-218, 2017.

B. Johnston, A. McEwan, and P. de Chazal. Semi-automated nasal PAP mask sizing using facial photographs, IEEE Engineering in Medicine and Biology Society, 1214– 1217, 2017.

Y. Cohn, J. Tian, and T. Kanade. Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal, 23(2):97-114, 2001.

M. Pantic and LJM. Rothkrantz. Automatic analysis of facial expressions: the state of the art. IEEE Trans. Pattern Anal. 22(12):1424-1445, 2000.

K. Liu, A. Weissenfeld , J. Ostermann, and X. Luo. Robust AAM building for morphing in an image-based facial animation system. Int. Conf. on Multimedia and Expo, 933- 936, 2008.

S. Ioannou, G. Caridakis, K. Karpouzis, and S. Kollias. Robust feature detection for facial expression recognition. Image Video Process, 5-5, 2007.

U. Park and AK. Jain. 3D face reconstruction from stereo images. Workshop on Video Processing for Security, 41-41, 2006.

AA. Salah, N. Alyüz, and L. Akarun. Registration of 3D face scans with average face models. Electron. Imag., 17(1), 2008.

N. Pears, T. Heseltine, and M. Romero. From 3D point clouds to pose-normalised depth maps. Comput. Vis, 89(2):152-176, 2010.

A. Lanitis, CJ. Taylor, and TF. Cootes. Automatic face idenitification system using flexible appearance models. Image Vis. Comput,13(5):393-401, 1995.

L. Wiskott, JM. Fellous, N. Kruger, and C. von der Malsburg. Face recognition by elastic bunch graph. IEEE Trans. Pattern Anal. Mach. Intell, 7:775-779, 1997.

P. Campadelli, R. Lanzarotti, and C. Savazzi. A feature-based face recognition system. Image Analysis and Processing, 68-73, 2003.

F. Dornaika and F. Davoine. Online appearance-based face and facial feature tracking. Pattern Recognition,814-817, 2004.

J. Cohn, A. Zlochower, JJJ. Lien, and T. Kanade. Feature-point tracking by optical flow discriminates subtle differences in faial expression. Automatic Face and Gesture Recognition, 396-401, 1998.

H. Çınar Akakın and B. Sankur. Analysis of head and facial gestures using facial landmark trajectories. Biometric ID Management and Multimodal Communication,105-113, 2009.

V. Kazemi and J. Sullivan. One millisecond face alignment with an ensemble of regression trees. IEEE Conference on Computer Vision and Pattern Recognition, 1867-1874, 2014.

C. Pintavirooj, F. S. Cohen and W. Iampa. Fingerprint Verification and Identification Based on Local Geometric Invariants Constructed from Minutiae Points and Augmented With Global Directional Filterbank Features. IEICE Transactions on Information and Systems, 97(6):1599-1613, 2014.

http://dlib.net/

E. Vezzetti, F. Marcolin and G. Fracastoro. 3D Face Recognition: An automatic Strategy based on Geometrical Descriptors and Landmarks. Robotics and Autonomous Systems 62:1768–1776, 2014.

Z. Yang and F.S. Cohen. Image registration and object recognition using affine invariants and convex hulls. IEEE Trans. Image Process, 8(7):934–946, 1999.

T. Chaichana, M. Sangworasil, C. Pintavirooj, S. Aootaphao. Acclerate a Dlt Motion Capture System with Quad-Tree Searching Scheme. International Symposium on Communications and Information Technology, 2006.

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Published

2020-08-24

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Section

Smart Innovative Technology for Future Industry and Multimedia Applications

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