Q. Chen, H. Wu, and M. Yachida. An Application of Fuzzy Theory: Face Detection. In Int. Workshop on Automatic Face and Gesture Recognition, pages 314-319, 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... 44, 116, 117, 118, 119] pattern based [13, 120, 121, 122, 12, 123, 124, 125, 126, 127, 128, 129, 130] colour based [90, 102, 103, 117, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147] motion based [114, 136, 140, 148] hybrid approaches miscellaneous [95, 102, 103, 110, 117, 140, 131, 136, 148, 143, 144, 149, 150, 151]. There now follows a brief review of each of these strategies. 7.5.1 Shape based Approaches Shape based systems obtain the location of faces using external contours. One of the earliest systems was implemented by Govindaraju et al. [107, 108, 109] in 1989, who fitted 3 curves to the jaw line. ....

....of another. Therefore the way in which the information is combined can be critical. A simple approach is to combine the components serially: the output of one becomes the input of another. However, this is probably not the optimum arrangement of fusion. Instead, fuzzy logic (see Chen et al. [117, 150]) Bayesian classifiers (see Yow [95] and Self Organising Feature Maps (see Takacs and Wechsler [110] are all candidates for fusion. Complex fusion of this kind is no easy task, and is indeed a research topic in itself see Chibelushi et al. [151] Therefore, within the scope of Part II only ....

Q. Chen, H. Wu, and M. Yachida. An Application of Fuzzy Theory: Face Detection. In Int. Workshop on Automatic Face and Gesture Recognition, pages 314-319, 1995.

....remains as an important problem to be solved. Recent work on face detection are attempted using various techniques: neural networks (Rowley et al. 9] shape statistics (Leung et al. 5] bandpass filtering (Graf et al. 3] ellipse fitting (Jacquin and Eleftheriadis [4] and colour (Wu et al. [11]) The neural networks and shape statistics approach works only for fronto parallel faces with little variation in viewpoint. The methods based on bandpass filtering and ellipse fitting works only for head and shoulder images with very little background clutter. Wu et al. s method of using colour ....

H. Wu, Q. Chen, and M. Yachida. An application of fuzzy theory: Face detection. In International Workshop on Automatic Face and Gesture Recognition, pages 314--319, Zurich, 1995.

....Faces are significantly characterized by their oval shape and specific skin color. For that reason, we segment the facial regions on the base of shape and color information. 3. 1 Segmentation based on color information The effectiveness of using color information was already shown in [4] and [17]. Mostly the primary components of Red, Green and Blue are used for segmentation [15] We have decided to consider the Hue Saturation Value (HSV) color space to extract skin color regions, because it is compatible to the human color perception. Alternatively the Hue Saturation Intensity (HSI) ....

H. Wu, Q. Chen and M. Yachida. An application of fuzzy theory: Face detection. In International Workshop on Automatic Face and Gesture Recognition, IEEE Computer Society, Swiss Informaticians Society, Swiss Computer Graphics Association et al., Zurich, Switzerland, June 26-28, 1995, pp. 314--319.

....with significant changes in viewpoint. More recent work on face detection are attempted using various techniques: neural networks (Rowley et al. 12] shape statistics (Leung et al. 8] bandpass filtering (Graf et al. 5] ellipse fitting (Jacquin and Eleftheriadis [6] and colour (Wu et al. [15]) The neural networks and shape statistics approach works only for fronto parallel faces with little variation in viewpoint. The methods based on bandpass filtering and ellipse fitting works only for head and shoulder images with very little background clutter. Wu et al. s method of using colour ....

H. Wu, Q. Chen, and M. Yachida. An application of fuzzy theory: Face detection. In International Workshop on Automatic Face and Gesture Recognition, pages 314--319, Zurich, 1995.

....operations. Afterwards, facial features are located by evaluating the minima and maxima of the topographic greylevel relief. 2. Segmentation of face like regions In the field of face localization, several approaches have been published using texture [4] shape [5] depth [6] and color information [11] or combinations of them. However, the detection of facial regions out of scenes with cluttered background is still a problem. Faces are significantly characterized by their oval shape and specific skin color. For that reason, we have chosen an approach based on these characteristics. The ....

H. Wu, Q. Chen, and M. Yachida. An application of fuzzy theory: Face detection. In International Workshop on Automatic Face and Gesture Recognition, pages 314--319, Zurich, Switzerland, June 26-28 1995.

....attempting to detect a facial region at a coarse resolution and subsequently to validate the outcome by detecting facial features at the next resolution level [4] Towards this goal, the method employs a hierarchical knowledge based pattern recognition system. Fuzzy theory has been used in [5] in order to cope with the inexact knowledge about the face and to improve the performance of the method. Mosaic images have been employed in detecting an unknown human face in input imagery and recognizing the facial expression as well [6] Another closely related method that is based on a ....

H. Wu, Q. Chen and M. Yachida, "An application of fuzzy theory:face detection," in Proc. of the Int. Workshop on Automatic Face- and Gesture- Recognition (M. Bichsel, Editor), pp. 314--319, Zurich, 1995.

....the number of components fixed. The assumption made here is that the number of components needed to accurately model an object s colour does not alter significantly with changing viewing conditions. Methods have been proposed for colour based detection and tracking of skincoloured objects (e.g. [1, 14 17]) In particular, a system constructed by Wren et al. 18] enabled tracking of entire people in controlled environments with static cameras. Each pixel in an image had an associated feature vector comprising spatial and colour components. These feature vectors were clustered, which led to a ....

H. Wu, Q. Chen, and M. Yachida, "An application of fuzzy theory: face detection," in IWAFGR, Zurich, June 1995, pp. 314--319.

....by using ellipse fitting or eigen silhouettes [14, 22] However, a robust method should incorporate information regarding the internal structure of faces. The property of facial symmetry has been used to align faces [14] Colour provides a useful cue through the detection of natural skin tones [23, 29] and texture measures have also been incorporated [5] The detection of local facial features (e.g. eyes, nose, lips) using photometric measurements appears to be unreliable and must be coupled with a model of the spatial arrangement of these features [2, 12] At low spatial resolution such an ....

H. Wu, Q. Chen, and M. Yachida. An application of fuzzy theory: face detection. In IWAFGR, pages 314--319, Zurich, June 1995.

....of the face like regions. This is done by applying morphological operations and minima localization to intensity images. 3.1. 1 Face localization and approximation In the field of face localization, approaches have been published using texture [7] depth [12] shape [11] and color information [24] or combinations of them. Still the detection of facial regions out of scenes with complex background is a problem. In our approach we take advantage of the skin specific color and the oval shape of faces. As discriminating color information we consider the attributes hue and saturation. The ....

H. Wu, Q. Chen, M. Yachida, "An Application of Fuzzy Theory: Face Detection, " Int. Workshop on Automatic Face- and Gesture Recognition, pp. 314-319, ed. Martin Bichsel, Zurich, Switzerland, June (1995).

....detectors via a statistical model to find the facial components for face locating. Their apprach was invariant with respect to translation, rotation, and scale. Besides, they could also handle partial occlusion of faces. Instead of the gray scale images, Sobottka and Pitas[23] and Chen et al.[24, 25, 26, 27, 28] located the poses of human faces and facial 3 features from color images. In [23] the oval shape of a face could be approximated by an ellipse in Hue Saturation Value(HSV) color space. Chen et al. 24, 25, 26, 27, 28] proposed a skin color distribution function on perceptually uniform color ....

....faces. Instead of the gray scale images, Sobottka and Pitas[23] and Chen et al. 24, 25, 26, 27, 28] located the poses of human faces and facial 3 features from color images. In [23] the oval shape of a face could be approximated by an ellipse in Hue Saturation Value(HSV) color space. Chen et al.[24, 25, 26, 27, 28] proposed a skin color distribution function on perceptually uniform color space to detect the face like region. The skin color regions in color images were modeled as several 2 D patterns and verified with the built face model by a fuzzy pattern matching approach. Most of the above mentioned ....

H. Wu, Q. Chen, and M. Yachida, "An application of fuzzy theory: Face detection", in Proc. International Workshop on Automatic Face and Gesture Recognition, Zurich, Switzerland, Jun. 1995, pp. 26--28.

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H. Wu, Q. Chen, and M. Yachida. An application of fuzzy theory: Face detection. In International Workshop on Automatic Face and Gesture Recognition, pages 314--319, Zurich, Switzerland, June 26-28 1995.

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H. Wu, Q. Chen, M. Yachida, "An Application of Fuzzy Theory: Face Detection," International Workshop on Automatic Face- and Gesture Recognition, pp. 314-319, ed. Martin Bichsel, Zurich, Switzerland, June 1995.

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