Kanade, T.: Computer Recognition of Human Faces. Birkhauser (1977)  Home/Search   Document Not in Database   Summary   Related Articles   Check

....assembly, food processing, quality control, manufacturing, modeling and simulation [International Journal of Machine Vision, Special Issue 1999] Face Recognition. People in computer vision and pattern recognition have been working in automatic recognition of human faces for more than 25 years [Kanade 1977], Turk and Pentland 1991] Recently there has been renewed interest in the problem due in part to numerous security applications ranging from identification of people in police databases to video based biometric person authentication, and identity verification at automatic teller machines. ....

Kanade, T. Computer Recognition of Human Faces. Birkhuser Verlag, Stuttgart, 1977.

....learned directly from example image sequences of the actions, bypassing the physical model. Feature based approaches. One of the earliest approaches to recognizing facial identity in images was based on a set of feature measurements such as nose length, chin shape, and distance between the eyes (Kanade, 1977; Brunelli Poggio, 1993) Lanitis, Taylor, Cootes, 1997) recognized identity, gender, and facial expressions by measuring shapes and spatial relationships of a set of facial features using a flexible face model. An advantage of the feature based approach is that it drastically reduces the ....

Kanade, T. (1977). Computer recognition of human faces. Basel & Stuttgart: Birkhauser Verlag.

....Security: Eye Detection and Text Detection. Before digital medical images in computer based patient record systems can be distributed online, it is necessary for confidentiality reasons to eliminate patient identification information that appears in the images. Face and eye detection algorithms [4, 15, 19] are at a mature stage of development in the computer vision community. For photographic medical images, it is necessary to use such an algorithm to detect and eliminate human eyes in order to protect patient privacy. Since the system we are developing deals with radiological images, such an ....

T Kanade, Computer Recognition of Human Faces, Birkhauser, 1977.

....variation (out of image plane rotation of the head) some scale variation was also present. For real time ability, we combine simple image features through a voting procedure for performing face recognition. 1. Introduction Face Recognition has been studied for over 20 years in computer vision [6, 7]. Since the beginning of the 1990s, the subject has become a major issue, mainly due to the important real world applications of face recognition: smart surveillance, secure access, telecommunications, digital libraries, medicine. a survey can be found in [2] On the theoretical side, face ....

T. Kanade. Computer Recognition of Human Faces. Basel and Stuttgart : Birkhauser, 1977.

....variation (out of image plane rotation of the head) some scale variation was also present. For real time ability, we combine simple image features through a voting procedure for performing face recognition. 1. Introduction Face Recognition has been studied for over 20 years in computer vision [6, 7]. Since the beginning of the 1990s, the subject has become a major issue, mainly due to the important real world applications of face recognition: smart surveillance, secure access, telecommunications, digital libraries, medicine. a survey can be found in [2] On the theoretical side, face ....

T. Kanade. Computer Recognition of Human Faces. Basel and Stuttgart : Birkhauser, 1977.

....points on the profile (such as the notch between the brow and the nose or the tip of the nose) and the angles between these points. For example, Kaya and Kobayashi [Kaya and Kobayashi, 1972] used Euclidean distances between manually identified points in the images to characterize the faces. Kanade [Kanade, 1977] used the distances and angles between eye corners, ends of the mouth, nostrils, and top of the chin, but the location of those facial features were extracted automatically by a program. More resent work used a combination of distance and angle measurements with local intensity patches. For ....

Kanade, T. (1977). Computer Recognition of Human Faces. Birkhauser, Basel and Stuttgart.

....later achieve a closed form solution for the similarity function we seek. Addressing the first problem is the subject of the remainder of this section, leading eventually to our first main result. The case we ve just considered corresponds to a generalization of the technique used for example in [6] where inverse variances are used to weight Euclidean distance providing similarity judgments. This amounts to assuming a diagonal covariance matrix, then forming the vector Q Gamma Y and selecting the Y that maximizes the probability of this difference given the zero mean normal density arising ....

T. Kanade, Computer Recognition of Human Faces. Birkhauser Verlag, Stuttgart Germany, 1977.

....stage between the universal and object specific levels. At this level, the processing depends on the class to which the object in the image is assumed to belong. For example, class level processing may include the extraction of facial features such as the location of the eyes, mouth and nose (Kanade, 1977; Craw et al. 1987; Yuille et al. 1989) Such processing is applicable to face images, but not to other objects. In general, classification can be hierarchical, that is, a given class of objects can belong in turn to a more inclusive class as well. For example, the face of an individual belongs ....

Kanade, T. (1977). Computer recognition of human faces. Birkhauser Verlag. Basel ans Stuttgart.

.... by humans and machines (see [18, 19, 20, 35, 92] for work on human perception of faces) The earliest work on machine recognition of faces appears in the mid seventies, where typical pattern classification techniques were used to measure and compare facial features attributes for recognition [47]. Not much work appeared in this area until the nineties, when the availability of increased computational power coupled with a commercial demand of face recognition systems made the problem computationally viable and commercially exciting. At present face recognition is perhaps the most widely ....

T. Kanade. Computer recognition of human faces. Birkhauser Verlag, 1977.

....with GWT representations was examined using PCA. We discuss our experimental results and draw a few preliminary conclusions. 1 Introduction Techniques for computer vision based automated face recognition can be largely divided into three categories: 3D model based [2] 2D geometric feature based [5, 7, 12], and 2D appearance based matching [13, 20, 23] We subscribe to the view that the appearance based approach is more promising whilst neither 3D models nor 2D geometric features can be extracted and matched robustly under changing viewing conditions, in particular, face pose changes [3, 9, 26] ....

T. Kanade. Computer recognition of human faces. Interdisciplinary Systems Res., 47, 1977.

....in illumination. For example, the edge map of the image (Davis, 1975; Marr and Hildreth, 1980; Haralick, 1984; Canny, 1986; Torre and Poggio, 1986; Deriche, 1987) is often considered as the basic image representation model for general object recognition and, in particular, for face recognition (Kanade, 1977; Wong et al. 1989; Govindaraju et al. 1989; Brunelli and Poggio, 1991) Other examples of image representations will be considered below later. The third approach to handle image variations that are due to illumination differences is by using as a model several images of the same object (face) ....

....The advantage of using an edge representation is that it is a relatively compact representation (compared with the full grey level image) and it is often insensitive to illumination changes for a variety of objects. Such edge representations were used by several face recognition systems (Kanade, 1977; Wong et al. 1989; Govindaraju et al. 1989; Brunelli and Poggio, 1991) The image filtered with 2D Gabor like functions: physiological and psychophysical evidence indicates that at the early stages of human visual processing the images are processed by local, multiple, and parallel channels ....

Kanade, T. (1977). Computer Recognition of Human Faces. Birkhauser Verlag. Basel and Stuttgart.

....reversed in testing relative to the order during learning. 1. 3 Computational Algorithms for Recognizing Faces in Images One of the earliest approaches to recognizing facial identity in images was based on a set of feature measurements such as nose length, chin shape, and distance between the eyes [106, 34]. An advantage of the feature based approach is that it drastically reduces the number of input dimensions. A disadvantage is that the specific image features relevant to the classification may not be known in advance, and vital information may be lost when compressing the image into a limited set ....

....learned directly from example image sequences of the actions, bypassing the physical model. Feature based approaches One of the earliest approaches to recognizing facial identity in images was based on a set of feature measurements such as nose length, chin shape, and distance between the eyes [106, 34]. Lanitis, Taylor, Cootes [115] recognized identity, gender, and facial expressions by 59 measuring shapes and spatial relationships of a set of facial features using a flexible face model. An advantage of the feature based approach is that it drastically reduces the number of input ....

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T. Kanade. Computer recognition of human faces. Birkhauser Verlag, Basel and Stuttgart, 1977.

....At the class based level, the processing depends on the class to which the object in the image is assumed to belong. For example, if the object is assumed to be a face, a class level processing stage may include the extraction of facial features such as the location of the eyes, mouth and nose (Kanade, 1977; Craw et al. 1987; Yuille et al. 1989) Our experiments revealed differences in generalization performance between upright and inverted faces suggesting the two generalization is not entirely universal in nature. The result also show substantial recognition capacity from a single (upright) face ....

Kanade, T. (1977). Computer recognition of human faces. Birkhauser Verlag. Basel ans Stuttgart.

....due to changes in pose, lighting, and facial expression. The applications are numerous, e.g. human computer interaction, smart surveillance and image retrieval in a database (see [5] for a survey) While earlier geometrical representations of faces focus on spatial distributions of facial features [10], photometric (i.e. image based) methods have recently been implemented, including iso density maps [16] or natural basis functions [22] view based methods such as template matching [4, 2] and principal components analysis [27, 20, 12] have been particularly successful. For most applications, ....

T. Kanade. Computer Recognition of Human Faces. Basel and Stuttgart : Birkhauser, 1977.

....to the eigen face space. Since the network uses the raw grey level images and no higher level knowledge about the face, the method might be very sensitive to illumination, pose, and facial expressions. Geometric Features One of the earliest works in face recognition has been described by Kanade [95] and was based on geometric features. Brunelli and Poggio [30] compared geometric features with templates for face recognition. 35 geometric features were extracted from the face which consisted of measures of the eyebrows, nose, mouth, chin, and face width. A sophisticated version of template ....

T. Kanade. Computer Recognition of Human Faces. Birkhaeuser, Basel and Stuttgart, 1977.

....manually completed for each face in the database. Human subjects were then asked to identify faces in databases ranging in size from 64 to 255 using 22 features. Interestingly, only 50 accuracy was obtained. Subsequent work addressed the problem of automatically extracting facial features. Kanade [7, 9] described a system which automatically extracted a set of facial features, computed a 16 dimensional feature vector based on ratios of distances (and areas) between facial features, and compared two faces based on a sum of distances. On a database of 20 faces, Kanade achieved a recognition rate ....

T. Kanade, Computer Recognition of Human Faces. Birkhauser Verlag, Stuttgart Germany, 1977.

....the algorithm achieved a correct localization rate of 95 in images where the face appeared quasi frontally. 1 Introduction The problem of face recognition has received considerable attention from the computer vision community, and a number of techniques have been proposed in the literature [3, 11, 12, 13, 14, 16, 17, 19]. However, in most of these studies the face was in a benign environment from which it could easily be extracted, or it was assumed to have been pre segmented. For any of these recognition algorithms to work in a general setting, we need a system that can reliably locate faces in cluttered scenes ....

T. Kanade. "Computer Recognition of Human Faces". Interdisciplinary Systems Research, 47, 1977.

....with GWT representations was examined using PCA. We discuss our experimental results and draw a few preliminary conclusions. 1 Introduction Techniques for computer vision based automated face recognition can be largely divided into three categories: 3D model based [2] 2D geometric feature based [5, 7, 12], and 2D appearancebased matching [13, 20, 23] We subscribe to the view that the appearance based approach is more promising whilst neither 3D models nor 2D geometric features can be extracted and matched robustly under changing viewing conditions, in particular, face pose changes [3, 9, 26] ....

T. Kanade. Computer recognition of human faces. Interdisciplinary Systems Res., 47, 1977.

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Kanade, T., Computer Recognition of Human Faces, Basel & Stuttgart, Birkhauser Verlag, 1977.

....efficient, and quantitative measurement of facial displays, we have used computer vision to develop an automated method of facial display analysis. Computer vision has been an active area of research for some 30 years (Duda Hart, 1973) early work included attempts at automated face recognition (Kanade, 1973, 1977). More recently, there is significant interest in automated facial display analysis by computer vision. One approach, initially developed for face recognition, uses a combination of principal components analysis (PCA) of digitized face images and artificial neural networks. High dimensional face ....

Kanade, T. (1977). Computer recognition of human faces. Stuttgart and Busel: Birkhauser Verlag.

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Kanade, T.: Computer Recognition of Human Faces. Birkhauser (1977)

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Kanade, T. (1977). "Computer Recognition of Human Faces". Birkhauser, Basel and Stuttgard.

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T. Kanade. Computer Recognition of Human Faces. Basel and Stuttgart: Birkhauser, 1973.

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T. Kanade. Computer recognition of human faces. Birkhauser Verlag, Stuttgart, Germany, 1977.

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T. Kanade. Computer Recognition of Human Faces. Birkhauser, Basel and Stuttgart, 1977.

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