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ABSTRACT: In this paper we describe a multi-modal ear and face biometric system. The system is comprised of two components: a 3D ear recognition component and a 2D face recognition component. For the 3D ear recognition, a series of frames is extracted from a video clip and the region of interest (i.e., ear) in each frame is independently reconstructed in 3D using Shape From Shading. The resulting 3D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series as a reference model and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. For the 2D face recognition, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model. Then, the response of facial images to a series of Gabor filters at the locations of facial landmarks are calculated. The Gabor features (attributes) are stored in the database as the face model for recognition. The similarity between the Gabor features of a probe facial image and the reference models are utilized to determine the best match. The match scores of the ear recognition and face recognition modalities are fused to boost the overall recognition rate of the system. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips (images). As a result, a rank-one identification rate of 100% was achieved using the weighted sum technique for fusion.
Image Processing (ICIP), 2009 16th IEEE International Conference on; 12/2009
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ABSTRACT: In this paper we describe a multi-modal ear and face biometric system. The system is comprised of two components: a 3D ear recognition component and a 2D face recognition component. For the 3D ear recognition, a series of frames is extracted from a video clip and the region of interest (i.e., ear) in each frame is independently reconstructed in 3D using Shape From Shading. The resulting 3D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series as a reference model and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. For the 2D face recognition, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model technique. Then, the response of facial images to a series of Gabor filters at the locations of facial landmarks are calculated. The Gabor features (attributes) are stored in the database as the face model for recognition. The similarity between the Gabor features of a probe facial image and the reference models are utilized to determine the best match. The match scores of the ear recognition and face recognition modalities are fused to boost the overall recognition rate of the system. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips (images). As a result, a rank-one identification rate of 100% was achieved using the weighted sum technique for fusion.
Safety, Security & Rescue Robotics (SSRR), 2009 IEEE International Workshop on; 12/2009
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ABSTRACT: In this paper we present a unified graph model, called Attributed Relational Graph (ARG), for multi-modal face modeling and recognition. Based on the ARG model, the 2-D and 3-D data are included in a single model. The developed ARG model consists of nodes, edges, and mutual relations. The nodes of the graph correspond to the landmark points that are extracted by an improved Active Shape Model (ASM) technique. Then, at each node of the graph, the responses of a set of log-Gabor filters to the facial image texture and shape information (depth values) are calculated; the filter responses are used to model the local structure of the face at each node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of mutual relations between the sides of the triangles are defined. The mutual relations boost the final performance of the system. The results of face matching using the 2-D and 3-D attributes and the mutual relations are fused at the score level. A rank-one identification rate of 99% is achieved by experimenting on the University of Miami face database.
Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on; 11/2008
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ABSTRACT: In this paper, we present a fully automated multi- modal (3-D and 2-D) face recognition system. For the 3-D modality, we model the facial image as a 3-D binary ridge image that contains the ridge lines on the face. We use the principal curvature to extract the locations of the ridge lines around the important facial regions on the range image (i.e., the eyes, the nose, and the mouth.) For matching, we utilize a fast variant of the iterative closest point to match the ridge image of a given probe image to the archived ridge images in the database. The main advantage of this approach is reducing the computational complexity by two orders of magnitude by relying on the ridge lines. For the 2-D modality, we model the face by an attributed relational graph (ARG), where each node of the graph corresponds to a facial feature point. At each facial feature point, a set of attributes is extracted by applying Gabor wavelets to the 2-D image and assigned to the node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of geometrical features that defines the mutual relations between the edges is extracted from the Delaunay triangles and stored in the ARG model. The similarity measure between the ARG models that represent the probe and gallery images is used for 2-D face recognition. Finally, we fuse the matching results of the 3-D and the 2-D modalities at the score level to improve the overall performance of the system. Different techniques for fusion, such as the Dempster-Shafer theory of evidence and weighted sum of scores are employed and tested using the facial images in the third experiment dataset of the Face Recognition Grand Challenge version 2.0.
IEEE Transactions on Information Forensics and Security 10/2008; · 1.34 Impact Factor
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ABSTRACT: In this paper, we presented algorithms to assess the quality of facial images affected by factors such as blurriness, lighting conditions, head pose variations, and facial expressions. We developed face recognition prediction functions for images affected by blurriness, lighting conditions, and head pose variations based upon the eigenface technique. We also developed a classifier for images affected by facial expressions to assess their quality for recognition by the eigenface technique. Our experiments using different facial image databases showed that our algorithms are capable of assessing the quality of facial images. These algorithms could be used in a module for facial image quality assessment in a face recognition system. In the future, we will integrate the different measures of image quality to produce a single measure that indicates the overall quality of a face image
IEEE Computational Intelligence Magazine 06/2007; · 3.37 Impact Factor
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ABSTRACT: We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. We then align a low resolution 2D mesh model to the selected features, adjust some of its vertices along the profile line using the profile view, increase its triangular vertices to a higher resolution, and re-project them back on the frontal image. Using the coordinates of the re-projected vertices and their corresponding disparities, we capture and compute the 3D facial shape variations using stereo vision. The final result is a deformed 3D model specific to a given subject's face. Application of the model in 3D face recognition validates the algorithm and shows a promising 98 % recognition rate
Image Processing, 2006 IEEE International Conference on; 11/2006
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ABSTRACT: In this paper, we present an improved active shape model (ASM) for facial feature extraction. The original ASM method developed by Cootes et al. highly relies on the initialization and the representation of the local structure of the facial features in the image. We use color information to improve the ASM approach for facial feature extraction. The color information is used to localize the centers of the mouth and the eyes to assist the initialization step. Moreover, we model the local structure of the feature points in the RGB color space. Besides, we use 2D affine transformation to align facial features that are perturbed by head pose variations. In fact, the 2D affine transformation compensates for the effects of both head pose variations and the projection of 3D data to 2D. Experiments on a face database of 50 subjects show that our approach outperforms the standard ASM and is successful in facial feature extraction.
Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on; 05/2006
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ABSTRACT: We present an automated algorithm to classify teeth in bitewing dental images, using Bayesian classification, and assign an absolute number to each tooth based on common numbering system used in dentistry. Fourier descriptors of the contours of the molar and the premolar teeth in bitewing images are used in the Bayesian classification of these two types of the teeth. Then, the spatial relation between the two types of the teeth is considered to number each tooth and correct the misclassification of some teeth in order to obtain high precision results. Experiments with 50 bitewing images containing more than 400 teeth show that our method is capable of classifying and assigning absolute index number to the teeth with high accuracy.
Image Processing, 2004. ICIP '04. 2004 International Conference on; 11/2004
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ABSTRACT: We present a multimodal approach for 3D face modeling and recognition from two frontal and one profile view stereo images of the face. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. We then align a low resolution mesh model to the selected features, adjust its vertices at the selected features and along the profile line using the profile view, increase its vertices to a higher resolution, and re-project them back on the frontal image. Using the coordinates of the re-projected vertices and their corresponding disparities, we capture and compute the 3D facial shape variations using triangulation. The final result is a deformed 3D model specific to a given subject's face. Application of the model in 3D face recognition validates the algorithm with a high recognition rate.
Biometric Consortium Conference, 2006 Biometrics Symposium: Special Session on Research at the;
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ABSTRACT: In this paper we present an approach for 3D face recognition from range data based on the principal curvature, k<sub>max</sub>, and Hausdorff distance. We use the principal curvature, k<sub>max</sub>, to represent the face image as a 3D binary image called ridge image. The ridge image shows the locations of the ridge lines around the important facial regions on the face (i.e. the eyes, the nose, and the mouth). We utilize Hausdorff distance to match the ridge image of a given probe to the created ridge images of the subjects in the gallery. For pose alignment, we extract the locations of three feature points, the inner corners of the two eyes and the tip of the nose using Gaussian curvature. These three feature points plus an auxiliary point in the center of the triangle, made by averaging the coordinates of the three feature points, are used for initial 3D face alignment. In the face recognition stage, we find the optimum pose alignment between the probe image and the gallery, which gives the minimum Hasusdorff distance between the two sets of features. This approach is used for identification of both neutral faces and faces with smile expression. Experiments on a public face database of 61 subjects resulted in 93.5% ranked one recognition rate for neutral expression and 82.0% for the faces with smile expression.
Image Processing, 2007. ICIP 2007. IEEE International Conference on;
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ABSTRACT: We present an algorithm for 3D face deformation and modeling using range data captured by a 3D scanner. Using only three facial feature points extracted from the range images and a 3D generic face model, the algorithm first aligns the 3D model to the entire range data of a given subject's face. Then each aligned triangle of the mesh model, with three vertices, is treated as a surface plane which is then fitted to the corresponding interior 3D range data, using least squares plane fitting. Via triangular vertices subdivisions, a higher resolution model is generated from the coordinates of the aligned and fitted model. Finally the model and its triangular surfaces are fitted once again resulting in a smoother mesh model that resembles and captures the surface characteristic of the face. Application of the final deformed model in 3D face recognition, using a publicly available database, shows promising results.
Image Processing, 2007. ICIP 2007. IEEE International Conference on;
