- Volume 15 Issue 1
DOI QR Code
Comparison of Computer and Human Face Recognition According to Facial Components
- Nam, Hyun-Ha (Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology) ;
- Kang, Byung-Jun (Technical Research Institute, Hyundai Mobis) ;
- Park, Kang-Ryoung (Division of Electronics and Electrical Engineering, Dongguk University)
- Received : 2011.09.19
- Accepted : 2011.11.22
- Published : 2012.01.31
Face recognition is a biometric technology used to identify individuals based on facial feature information. Previous studies of face recognition used features including the eye, mouth and nose; however, there have been few studies on the effects of using other facial components, such as the eyebrows and chin, on recognition performance. We measured the recognition accuracy affected by these facial components, and compared the differences between computer-based and human-based facial recognition methods. This research is novel in the following four ways compared to previous works. First, we measured the effect of components such as the eyebrows and chin. And the accuracy of computer-based face recognition was compared to human-based face recognition according to facial components. Second, for computer-based recognition, facial components were automatically detected using the Adaboost algorithm and active appearance model (AAM), and user authentication was achieved with the face recognition algorithm based on principal component analysis (PCA). Third, we experimentally proved that the number of facial features (when including eyebrows, eye, nose, mouth, and chin) had a greater impact on the accuracy of human-based face recognition, but consistent inclusion of some feature such as chin area had more influence on the accuracy of computer-based face recognition because a computer uses the pixel values of facial images in classifying faces. Fourth, we experimentally proved that the eyebrow feature enhanced the accuracy of computer-based face recognition. However, the problem of occlusion by hair should be solved in order to use the eyebrow feature for face recognition.
Supported by : Korea Research Council of Fundamental Science & Technology
- G. Davies, H. Ellis, and J. Shepherd, "Cue Saliency in Faces as Assessed by the `Photofit' Technique," Perception, Vol.6, pp. 263- 269, 1977. https://doi.org/10.1068/p060263
- N. D. Haig, "Exploring Recognition with Interchanged Facial Features," Perception, Vol.15, pp. 505-512, 1986. https://doi.org/10.1068/p150505
- I. H. Fraser, G. L. Craig, and D. M. Parker, "Reaction Time Measures of Feature Saliency in Schematic Faces," Perception, Vol.19, pp. 661-673, 1990. https://doi.org/10.1068/p190661
- J. Sadrô, I. Jarudi, and P. Sinhao, "The Role of Eyebrows in Face Recognition," Perception, Vol.32, pp. 285-293, 2003. https://doi.org/10.1068/p5027
- A. J. O'Toole, P. J. Phillips, F. Jiang, J. Ayyad, N. Penard, and H. Abdi, "Face Recognition Algorithm Surpass Humans Matching Faces over Changes in Illumination," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.29, pp. 1642-1646, 2007. https://doi.org/10.1109/TPAMI.2007.1107
- R. Verma, C. Davatzikosa, J. Lougheadb, T. Indersmittenb, R. Huc, C. Kohlerb, R. E. Gurb, and R. C. Gurb, "Quantification of Facial Expressions Using High-Dimensional Shape Transformations," Journal of Neuroscience Methods, Vol.141, pp. 61-73, 2005. https://doi.org/10.1016/j.jneumeth.2004.05.016
- R. C. Gur, R. Sara, M. Hagendoorn, O. Marom, P. Hughett, L. Macy, T. Turner, R. Bajcsy, A. Posner, and R. E. Gur, "A Method for Obtaining 3-Dimensional Facial Expressions and Its Standardization for Use in Neurocognitive Studies," Journal of Neuroscience Methods, Vol.115, pp. 137-143, 2002. https://doi.org/10.1016/S0165-0270(02)00006-7
- D.-L. Maeng, B.-W. Hong, and S.-J. Kim, "Evaluation of Face Recognition System Based on Scenarios," Journal of Korea Multimedia Society, Vol.13, No.4, pp. 487-495, 2010.
- A. O'Toole and M. Tistarelli, "Face Recognition in Humans and Machines," Handbook of Remote Biometrics for Surveillance and Security, London, Springer, pp. 111-153, 2009.
- M. Turk and A. Pentland, "Eigenfaces for Recognition," Journal of Cognitive Neuroscience, Vol.3, pp. 71-86, 1991. https://doi.org/10.1162/jocn.1922.214.171.124
- M. Kirby and L. Sirovich, "Application of the Karhnen-Loeve Procedure for the Characterization of Human Faces," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.12, pp. 103-108, 1990. https://doi.org/10.1109/34.41390
- R. Gottumukkal and V. K. Asari, "An Improved Face Recognition Technique Based on Modular PCA Approach," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.28, pp. 2037-2041, 2003.
- H. Zhao, P. C. Yuen, and J. T. Kwok. "A Novel Incremental Principal Component Analysis and Its Application for Face Recognition," IEEE Trans. on Systems Man and Cybernetics, Part B: Cybernetics, Vol.36, pp. 873-886, 2006. https://doi.org/10.1109/TSMCB.2006.870645
- H. Zou, T. Hastie, and R. Tibshirani. "Sparse Principal Component Analysis," Journal of Computational and Graphical Statistics, Vol.15, pp. 262-286, 2006. https://doi.org/10.1198/jcgs.2006.s7
- H. Zhao and P.C. Yuen "Incremental Linear Discriminant Analysis for Face Recognition," IEEE Trans. on Systems Man and Cybernetics, Part B: Cybernetics, Vol.38, pp. 210-221, 2008. https://doi.org/10.1109/TSMCB.2007.908870
- J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos "Face Recognition Using LDA-based Algorithms," IEEE Trans. on Neural Networks, Vol.14, pp. 195-200, 2003. https://doi.org/10.1109/TNN.2002.806647
- L. Shen, L. Bai, and F. Michael, "Gabor Wavelets and General Discriminant Analysis for Face Identification and Verification," Image and Vision Computing, Vol.25, pp. 553-563, 2007. https://doi.org/10.1016/j.imavis.2006.05.002
- G. Baudat and F. Anouar, "Generalized Discriminant Analysis Using A Kernel Approach," IEEE Trans. on Neural Networks, Vol.12, pp. 2385-2404, 2000.
- K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, "An Introduction to Kernel Based Learning Algorithms," IEEE Trans. on Neural Networks, Vol.12, pp.181-201, 2001. https://doi.org/10.1109/72.914517
- S. Mika, G. Ratsch, J. Weston, B. Scholkof, and K. R. Muller "Fisher Discriminant Analysis with Kernels," Proc. the IEEE Workshop on Neural Networks for Signal Processing, Vol.IX, pp. 41-48, 1999.
- L. Bao, P. Shyang, and L. Ming, "Face Recognition Using Gabor-based Complete Kernel Fisher Discriminant Analysis with Fractional Power Polynomial Models," Neural Computing and Application, Vol.18, pp. 613-621, 2009. https://doi.org/10.1007/s00521-009-0272-0
- S. Mika, G. Ratsch, B. Scholkofm A. Smola, J. Weston, and K. R. Muller, "Invariant Feature Extraction and Classification in Kernel Spaces," Advanced in Neural Information Processing Systems, Vol.12, MIT Press, pp. 526-532, 2000.
- O. Deniz, M. Castrillón, and M. Hernandez, "Face Recognition Using Independent Component Analysis and Support Vector Machines," Pattern Recognition Letters, Vol.24, pp. 2153-2157, 2003. https://doi.org/10.1016/S0167-8655(03)00081-3
- H. K. Ekenel and R. Stiefelhagen, "Analysis of Local Appearance Based Face Recognition: Effects of Feature Selection and Feature Normalization," Proc. the IEEE CVPR Biometrics Workshop, New York, USA, June 2006.
- F. Y. Shih and C. Chuang. "Automatic Extraction of Head and Face Boundaries and Facial Features," Information Sciences, Vol. 158, pp. 117-130, 2004. https://doi.org/10.1016/j.ins.2003.03.002
- K. Sobottka and I. Pitas "A Fully Automatic Approach to Facial Feature Detection and Tracking," Proc. Audio-and Video based Biometric Person Authentication, Vol.1206, pp. 77-84, 1997. https://doi.org/10.1007/BFb0015982
- M. Zobe, A. Gebhard, D. Paulus, J. Denzler, and H. Niemann, "Robust Facial Feature Localization by Coupled Features," Proc. the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, France, pp. 2-7, 2000.
- L. Wiskott, J. M. Fellous, and C. V. der Malsburg, "Face Recognition by Elastic Bunch Graph Matching," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.19, pp. 775-779. 1997. https://doi.org/10.1109/34.598235
- B. Abboud, F. Davoine, and M. Dang "Facial Expression Recognition and Synthesis Based on An Appearance Model," Signal Processing: Image Communication, Vol.19, pp. 723-740, 2004.
- F. Tang and B. Deng "Facial Expression Recognition Using AAM and Local Facial Features," Proc. the third International Conference on Natural Computation, Vol.4, pp. 632-635, 2007.
- S. L. Gallou, G. Breton, C. Garcia, and R. Seguier, "Distance Maps: A Robust Illumination Preprocessing for Active Appearance Models," Proc. the International Conference on Computer Vision Theory and Applications, Vol.2, pp. 35-40, 2006.
- P. Viola and M. Jones, "Robust Real-time Face Detection," International Journal of Computer Vision, Vol.57, No.2, pp. 137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
- Open Computer Vision Library, http://opencvlibrary.sourceforge.net/(accessed November 29, 2011).
- Y. Cheon and D. Kim, "Natural Facial Expression Recognition Using Differential-AAM and Manifold Learning," Pattern Recognition, Vol.42, pp. 1340-1350, 2009. https://doi.org/10.1016/j.patcog.2008.10.010
- G. Edwards, C. Taylor, and T. Cootes, "Active Appearance Models," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.23, pp. 681-685, 2001. https://doi.org/10.1109/34.927467
- I. Matthews and S. Baker, "Active Appearance Models Revisited," International Journal of Computer Vision, Vol.60, pp. 135-164, 2004. https://doi.org/10.1023/B:VISI.0000029666.37597.d3
- K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York, 1972.
- K. Chung, S. C. Kee, and S. R. Kim, "Face Recognition Using Principal Component Analysis of Gabor Filter Responses," Proc. International Workshop on Recognition Analysis and Tracking of Faces and Gestures in Real-Time System, pp. 53-57, 1999.
- Eigenface-based Facial Recognition, http:// openbio.sourceforge.net/resources/eigenfaces/eigenfaces-html/facesOptions.html (accessed on November 29, 2011).
- J. Wayman, "Technical Testing and Evaluation of Biometric Identification Devices," In Biometrics: Personal Identification in Networked Society, Kluwer Academic, Netherlands, pp. 345-368, 1999.
- A. J. Mansfield and J. L. Wayman, Best Practices in Testing and Reporting Performance of Biometric Devices, UK Government Biometrics Working Group, England, 2002.
- Tracking and Face Recognition of Multiple People Based on GMM, LKT and PCA vol.15, pp.4, 2012, https://doi.org/10.9717/kmms.2012.15.4.449