MAP Estimation of Chin and Cheek Contours in Video Sequences
- PDF / 1,337,627 Bytes
- 10 Pages / 600 x 792 pts Page_size
- 71 Downloads / 171 Views
MAP Estimation of Chin and Cheek Contours in Video Sequences Markus Kampmann Ericsson Research, Ericsson Allee 1, 52134 Herzogenrath, Germany Email: [email protected] Received 28 December 2002; Revised 8 September 2003 An algorithm for the estimation of chin and cheek contours in video sequences is proposed. This algorithm exploits a priori knowledge about shape and position of chin and cheek contours in images. Exploiting knowledge about the shape, a parametric 2D model representing chin and cheek contours is introduced. Exploiting knowledge about the position, a MAP estimator is developed taking into account the observed luminance gradient as well as a priori probabilities of chin and cheek contours positions. The proposed algorithm was tested with head and shoulder video sequences (image resolution CIF). In nearly 70% of all investigated video frames, a subjectively error free estimation could be achieved. The 2D estimate error is measured as on average between 2.4 and 2.9 pel. Keywords and phrases: facial feature extraction, model-based video coding, parametric 2D model, face contour, face model.
1.
INTRODUCTION
Techniques for estimation of facial features like eyes, mouth, nose, eyebrows, chin and cheek contours are essential for various types of applications [1, 2, 3, 4, 5, 6, 7, 8]. For facial recognition applications, features are estimated and used for recognition, authentification, and differentiation of human faces [7, 9, 10]. In multimedia data bases and information systems, facial feature estimation is required for analysis and indexing of human facial images. For specific video coding schemes like model-based video coding [11, 12, 13] (also sometimes called semantic video coding [14, 15] or objectbased video coding [16, 17, 18]), facial feature estimation is also required. The estimated facial features are used for adaptation of a 3D face model to a person’s face as well as for the determination of facial expressions [19, 20, 21, 22, 23]. In this paper, the estimation of chin and cheek contours is discussed. The estimation of chin and cheek is one of the most difficult tasks of facial feature estimation, especially that the chin contour is in many cases little visible. Furthermore, shadows, variations of the skin color, clothing, and double chin can complicate the estimation procedure. Rotations of the head (especially to the side) result in strong variations of the chin and cheek’s shape and position. In this paper, head and shoulder video sequences are considered which are typical for news, videophone, or video conferencing sequences. Assuming a typical spatial resolution like the CIF format (352 × 288 luminance pels), the face size is quite small in those video sequences (with a typical face width from 40 to
70 pels). Taken this into account, the estimation of chin and cheek contours is further complicated. In order to overcome these problems of chin and cheek contours estimation, the usage of a priori knowledge about these features is necessary. On one hand, knowledge about the typical s
Data Loading...
