Facial Feature Tracking via Evolutionary Multiobjective Optimization

Facial feature tracking for model–based coding has evolved over the past decades. Of particular interest is its application in very low bit rate coding in which optimization is used to analyze head and shoulder sequences. We present the results of a computational experiment in which we apply a combination of non-dominated sorting genetic algorithm and a deterministic search to find optimal facial animation parameters at many bandwidths simultaneously. As objective functions are concerned, peak signal-to-noise ratio is maximized while the total number of facial animation parameters is minimized. Particularly, the algorithm is tested for efficiency and reliability. The results show that the overall methodology works effectively, but that a better error assessment function is needed for future study. DOI: 10.4018/jaec.2010010104 58 International Journal of Applied Evolutionary Computation, 1(1), 57-71, January-March 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. expression. Modern facial models can recreate a human likeness with remarkable clarity. Figure 1 shows three reconstructed facial models and the original frames. Using a model to transmit video opens the possibility for transmission using dynamic bandwidths (i.e., sending video at different bandwidths dynamically depending on the conditions of the transmission channel). In transform based coding, dynamic bandwidth means increasing or decreasing the compression thresholds, reducing video quality when the compression is high. In MBFC, dynamic bandwidth means sending a dynamic number of parameters of the face, resulting in less movement of the model at high compression but not necessarily less quality. This application promises to revolutionize video telephony and teleconferencing by drastically reducing the bandwidth required for transmission (Eisert, 2003). But there is no free lunch. The bandwidth reductions in MBFC must be paid for in computational analysis. Despite the advances in facial analysis, current work has been limited in several aspects. Before MBFC can be adequately implemented, the following factors need addressing: 1. The limitations of gradient-based optimization. This type of analysis, while showing promise for real-time implementation, inherently relies upon a gradient approximation. This approximation limits the problem scope to facial sequences involving movements built into the gradient approximation training (i.e., small head movements). 2. The use of static facial parameters. Current algorithms use a hand selected set of animation parameters on the face, or use all facial animation parameters. It is unclear if bandwidth could be further reduced using a dynamic set of animation parameters, or what the ideal set of parameters is that adequately represents all facial animation sequences. 3. The prohibitive use of computationally complex algorithms. The use of direct methods to analyze head and shoulder sequences in real time has been completed only by reducing the number of animation parameters optimized. The resultant frames are not considered high enough quality to be realistically rendered. In this article, we address all of the stated concerns by formulating the analysis and synthesis into a multiobjective optimization problem (MOP). In short, we want to design a facial coder that simultaneously finds a high Figure 1. Selected frames of a model-based video. Top: the original video frames. Bottom: frames synthesized from a model based coder. The frames are generated using a facial model; they are rendered sequences derived from stretching a facial “texture” over the three dimensional facial wireframe. See (Eisert, 2000). 13 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/facial-feature-tracking-viaevolutionary/40904?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Computer Science, Security, and Information Technology. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2