R. Chellapa, C. Wilson and S. Sirohey. "Human and machine recognition of faces: a survey". Proceedings of the IEEE , 83(5), 1995, pp. 705-741.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....a database which included faces rotated up to about ## in depth. The component system clearly outperformed both global systems on all tests. 1. Introduction Over the past 20 years numerous face recognition papers have been published in the computer vision community; a survey can be found in [4]. The number of realworld applications (e.g. surveillance, secure access, human computer interface) and the availability of cheap and powerful hardware also lead to the development of commercial face recognition systems. Despite the success of some of these systems in constrained scenarios, the ....

R. Chellapa, C. Wilson, and S. Sirohey. Human and machine recognition of faces: a survey. Proceedings of the IEEE, 83(5):705--741, 1995.

....content analysis, given a large number of applications like image retrieval in databases, face recognition or content based image coding. The automatic detection of human faces in images with complex background is an important preliminary task for these applications (see for example Chellapa et al. [1]) A problem closely related to face detection is face recognition. One of the basic approaches in face recognition is the eigenspace decomposition (e.g. Turk and Pentland [8] The image under consideration is projected into a low dimensional feature space that is spanned by the eigenvectors of ....

Chellapa R., Wilson C. L. and Sirohey S., 1995, "Human and machine recognition of faces: A survey", Proc. IEEE, 83(5), 705--740

....the face even under small occlusions. Therefore, a systematic approach, keeping in mind both the robustness and the computational complexity of the algorithm is called for. Various methods have been proposed in the literature for face detection. Important techniques include template matching [1], neural network [2] feature based methods [3 7] motion based [4] 8] and face space methods [9] In this paper, a method to detect and track face(s) in color image sequences using skin color analysis and connected operators is described. Section 2 deals with skin color modeling and its use ....

Chellapa, Wilson, Sirohey, Human and Machine Recognition of Faces: A Survey, Proceedings of the IEEE, Vol. 83, No. 5, pp. 705-740, May 1995.

....secured access. These categories are further detailed below. 3.1. Innovative Multimodal Verification Techniques Speech and face recognition have exhibited a tremendous growth for more than two decades. A critical survey of the literature related to human and machine face recognition is found in [4], and in [2,5] for speech recognition speaker recognition. Within the M2VTS project, even if more emphasis was put on face recognition and speaker verification, other important modalities related to image were studied. In summary, the key techniques developed include: Frontal face recognition ....

R. Chellapa, C.L. Wilson, and S. Sirohey, Human and machine recognition of faces: A survey, Proceedings of the IEEE, vol. 83, no. 5, pp. 705-740, May 1995

....The processing of face images receives more and more attention in the field of image analysis. There are many different problems and applications which base on the processing of such images. The tasks of facial image analysis include the face localization [15, 9] the recognition of human faces [12, 2, 13], and the analysis of mimics or facial expressions [16, 10, 14] Applications exist, for instance, for access control of rooms and buildings [5, 7] the indexing of police mugshots [3] or medical tasks [1] Another important application results from object based image coding and transmission[6, ....

A. Wilson, C.L. Wilson, S. Sirohey, and C.S. Barnes. Human and machine recognition of faces: A survey. Technical Report CS-TR3339, University of Maryland, College Park, August 1994.

....the face. This usually achieved using ei ther a triangulation with the eyes (or nose) which are more easily located [27] or by finding an area with high edge content in the lower half of the face region[12] Given the large amount of research already carried out in face locating recognition[4], many researches in AVSR opt to skip the stage and start working with pre cropped mouth images (e.g. 11] 20] This allows for a relatively quicker progression for researchers beginning work in this area. 2. Once the mouth region is found, either automatically or by hand, useful lip features ....

R. Chelappa, C. Wilson, and S. Sirobey. Human and machine recognition of faces: A survey. In Proceedings of the IEEE, volume 83(5), pages 705-739, 1995.

....were used in a database of 288 images. A recognition accuracy of 96 was achieved for frontal views, 96 for 45 # views, and 100 for profile views. This compares favorably with visible light techniques. 2 Introduction Automated face recognition is a well studied problem in computer vision [4]. Its current applications include security (ATM s, computer logins, secure building entrances) criminal photo ( mug shot databases, and human computer interfaces. One of the more successful techniques of face recognition is principle component analysis, and specifically eigenfaces [1, 2, 3] In ....

Chellapa, R., et. al., (1994) Human and Machine Recognition of Faces: A Survey, UMCP CS-TR-3339.

....the gray level intensity) Several similarity measures and pre processing techniques have been used to deal with image variations due to light condition, head pose, and facial expression. Chellapa, Wilson, and Sirohey have reviewed the research efforts on face recognition and other related issues [12]. A major difficulty in face recognition comes from the appearance variation of the face due to facial expressions. In the particular case of the mug shot problem, elastic graph matching techniques outperform other methods by allowing some deformation to deal with facial expression. However, the ....

R. Chellapa, C. L. Wilson, and S. Sirohey, "Human and Machine Recognition of Faces: A Survey", Proceedings of the IEEE, vol. 83, #5, May 1995.

....authentication algorithms indicates that the morphological dynamic link architecture with discriminatory power coefficients is the best algorithm with respect to the equal error rate achieved. I. Introduction Automated face recognition has exhibited a tremendous growth for more than two decades [1]. An approach that exploits both the grey level information and the geometrical one is the so called Dynamic Link Architecture (DLA) 2] 5] The algorithm is split in the training and recall phase. In the training phase, a sparse grid for each person included in the reference set is built. The ....

R. Chellapa, C.L. Wilson, and S. Sirohey, "Human and machine recognition of faces: A survey," Proceedings of the IEEE, vol. 83, no. 5, pp. 705-740, May 1995.

....for the following reasons: universality, collectability and acceptability [1] In the following, a brief overview of related previous work is given, and the objectives of our work are outlined. A. Previous work A comprehensive survey of human and machine recognition techniques can be found in [2, 3]. There are several approaches in developing face recognition systems. For example, one approach employs linear projections of face images (treated as 1 D vectors) using either principal component analysis (PCA) 4] or linear discriminant analysis (LDA) 5, 6, 7] PCA and LDA are parametric ....

R. Chellapa, C.L. Wilson, and S. Sirohey, "Human and machine recognition of faces: A survey," Proceedings of the IEEE, vol. 83, no. 5, pp. 705-740, May 1995.

....database. 1. INTRODUCTION Automated face recognition has exhibited a tremendous growth for more than two decades. Many techniques for face recognition have been developed whose principles span several disciplines, such as image processing, pattern recognition, computer vision and neural networks [1]. The increasing interest in face recognition is mainly driven by application demands, such as nonintrusive identification and verification for credit cards and automatic teller machine transactions, nonintrusive access control to buildings, identification for law enforcement, etc. A well known ....

R. Chellapa, C.L. Wilson, and S. Sirohey, "Human and machine recognition of faces: A survey," Proceedings of the IEEE, vol. 83, no. 5, pp. 705--740, May 1995.

....the non face regions. Thus, image quality can be greatly improved in face regions at the cost of reduced quality in background regions, resulting in better overall subjective quality for many sequences. 1. INTRODUCTION There exist many different face tracking algorithms in the research literature [2 3], with applications ranging from human computer interaction interface to video conferencing. One common technique used by many of the face tracking systems for color image or video is flesh tone color matching [1,4 6] However, different applications impose different constraints on the ....

R. Chellapa, C. Wilson and S. Sirohey, "Human and Machine Recognition of Faces: A Survey," Proceedings of IEEE, vol. 83, no. 5, pp. 705--740, 1995.

....1. INTRODUCTION Automated face recognition has exhibited a tremendous growth for more than two decades. Many techniques for face recognition have been developed whose principles span several disciplines, such as image processing, pattern recognition, computer vision and neural networks [1]. The increasing interest in face recognition is mainly driven by application demands, such as nonintrusive identification and verification for credit cards and automatic teller machine transactions, nonintrusive access control to buildings, identification for law enforcement, etc. A well known ....

R. Chellapa, C.L. Wilson, and S. Sirohey, "Human and machine recognition of faces: A survey," Proceedings of the IEEE, vol. 83, no. 5, pp. 705--740, May 1995.

....1. INTRODUCTION Automated face recognition has exhibited a tremendous growth for more than two decades. Many techniques for face recognition have been developed whose principles span several disciplines, such as image processing, pattern recognition, computer vision and neural networks [1]. The increasing interest in face recognition is mainly driven by application demands, such as nonintrusive identification and verification for credit cards and automatic teller machine transactions, nonintrusive access control to buildings, identification for law enforcement, etc. A well known ....

R. Chellapa, C.L. Wilson, and S. Sirohey, "Human and machine recognition of faces: A survey," Proceedings of the IEEE, vol. 83, no. 5, pp. 705--740, May 1995.

.... [6] 3D models [7] hand crafted shape models [8] local non linear shape manifolds[9] and models based on elastic meshes coubled with local intensity pattern descriptions[10] Compehensive literature reviews of these techniques and other techniques related to face interpretation can be found in [11,12,13]. The success of a model based approach relies on the quality of the face model used. In general the models must fulfill two main criteria: generality and specificity. General models are those that account for all possible sources of appearance variation in face images. Specific models constrain ....

R. Chellapa, C.L. Wilson and S. Sirohey. Human and machine Recognition of Faces: A Survey. Procs of the IEEE, 83, no 5, 1995.

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R. Chellapa, C. Wilson and S. Sirohey. "Human and machine recognition of faces: a survey". Proceedings of the IEEE , 83(5), 1995, pp. 705-741.

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Chellapa P., Wilson C., and Sirohey S. "Human and Machine Recognition of Faces: A Survey". Proc. IEEE, vol. 83, no. 5, pp. 705-740, 1995.

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R. Chellapa, C. L. Wilson, and S. Sirohey, "Human and machine recognition of faces: A survey," Proceedings of the IEEE, vol. 83, pp. 705--740, 1995.

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CHELLAPA , R., WILSON , C., SIROHEY, S., "Human and machine recognition of faces: A Survey," Proceeding of the IEEE, 83 (5), May 1995, pp. 705-740.

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R. Chellapa, C.L. Wilson, and S. Sirohey, "Human and machine recognition of faces: A survey," Proceedings of the IEEE, vol. 83, no. 5, pp. 705--740, May 1995.

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R. Chellapa, C.L. Wilson, and S. Sirohey, \Human and machine recognition of faces: A survey", Proc. of the IEEE, 1995, 83(5), pp. 705-740.

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R. Chellapa, C. Wilson, S. Sirohey, Human and machine recognition of faces: a survey, Proc. IEEE 83 (5) (1995) 705--741.

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R. Chelleppa, C. L. Wilson and S. Sirobey. "Human and machine recognition of faces : A survey." Proceedings of the IEEE. vol. 83, no. 5, May 1995, pp. 705 740.

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Chelappa, R., Wilson, C., and Sirobey, S. (1995). Human and machine recognition of faces: A survey. In Proceedings of the IEEE, volume 83(5), pages 705-739.

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Chelappa, R., Wilson, C., and Sirohey, S. (1995). Human and machine recog- nition of faces: A survey. In Proceedings of the IEEE, volume 83(5), pages 705-739.

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