Abstract
We present a coarsely locating little points and finely locating many points approach for face alignment. This cascade structure replies to 2 problems existing all the time in face alignment: the initialization and great accuracy difference between inner points and outline points. First, we adopt DCNN to coarsely localize 5 points: two pupils, nose and two mouth corners. Second, based on shape initialization of coarse location, using SDM with extracting simplified SIFT features, we finely localizes 49 inner points and 17 outline points. Experiments on CAS-PEAL-R1 and FERET database show that our approach is accurate and robust. The proposed method achieves 99.23% localization accuracy of eyes on CAS-PEAL-R1.
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References
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483. IEEE Press, Portland (2013)
Zhou, E., Fan, H., Cao, Z., Jiang, Y., Yin, Q.: Extensive facial landmark localization with coarse-to-fine convolutional network cascade. In: 2013 IEEE International Conference on Computer Vision Workshops, pp. 386–391. IEEE Press, Sydney (2013)
Xiong, X.H., Fernando, D.F.: Supervised descent method and its applications to face alignment. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 532–539. IEEE Press, Portland (2013)
Lowe, D.G.: Object recognition from local scale-invariant features. In: 10th IEEE International Conference on Computer vision, pp. 1150–1157. IEEE Press, Kerkyra (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Liang, L., Xiao, R., Wen, F., Sun, J.: Face alignment via component-based discriminative search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 72–85. Springer, Heidelberg (2008)
Face SDK. http://research.microsoft.com/en-us/projects/facesdk
Valstar, M., Martinez, B., Binefa, X., Pantic, M.: Facial point detection using boosted regression and graph models. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 72–85. IEEE Press, San Francisco (2010)
BioID Face Database. https://www.bioid.com/About/BioID-Face-Database
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© 2015 Springer International Publishing Switzerland
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Tang, Q., Zhang, Q., Zhang, X., Cai, Z., Zhang, X. (2015). A DCNN and SDM Based Face Alignment Algorithm. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_12
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DOI: https://doi.org/10.1007/978-3-319-25417-3_12
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