Abstract
To complete facial feature points tracking accurately, we propose an improved active appearance model (AAM), which is online reference appearance model (ORAM) with the subspace update mechanism. The online update reference texture models are combined with the conventional AAM. When the model fitting is satisfactory while tracking, we update the AAM texture model via the incremental learning method and simultaneously update the reference texture model with the current frame appearance information. The ORAM-fitting algorithm is established based on simultaneously inverse compositional (SIC). To reduce the cumulative error produced in the tracking process, we provide the texture subspace reset mechanism with the original stability frames. Compared with other AAM algorithms that require multiple training images, ORAM achieves accurate tracking results with no training data needed. Experiments indicate the proposed method can complete face tracking accurately and quickly.
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References
Matthews I, Baker S (2004) Active appearance models revisited. Int J Comput Vis 60(2):135–164
Edwards GJ, Cootes TF, Taylor CJ (1998) Face recognition using active appearance models. In: Computer vision ECCV’98. Springer, Heidelberg, pp 581–595
Lan Y, Theobald BJ, Harvey R, Ong EJ, Bowden R (2010) Improving visual features for lip-reading. In: Proceedings of international conference on auditory-visual speech processing, vol 201
Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59
Baker S, Matthews I (2004) Lucas-kanade 20 years on: a unifying framework. Int J Comput Vis 56(3):221–255
Yu MS, Li SF (2006) Tracking facial feature points with statistical models and Gabor wavelet. In: Proceeding of fifth Mexican international conference on artificial intelligence, MICAI’06. IEEE, pp 61–67
Sung J, Kim D (2009) Adaptive active appearance model with incremental learning. Pattern Recogn Lett 30(4):359–367
Goodall C (1991) Procrustes methods in the statistical analysis of shape. J Roy Stat Soc. Ser B (Methodol), pp 285–339
Hall PM, Marshall AD, Martin RR (1998) Incremental eigenanalysis for classification. In: BMVC, vol 98, pp 286–295
Liu X (2010) Video-based face model fitting using adaptive active appearance model. Image Vis Comput 28(7):1162–1172
Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant No. 61104213 and the National Natural Science Foundation of Jiangsu Province under Grant No. BK2011146.
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Guo, X., Chen, Y. (2014). Facial Feature Points Tracking with Online Reference Appearance Model. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_24
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DOI: https://doi.org/10.1007/978-3-642-54924-3_24
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