315 research outputs found
Comparing landmarking methods for face recognition
Good registration (alignment to a reference) is essential for accurate face recognition. We use the locations of facial features (eyes, nose, mouth, etc) as landmarks for registration. Two landmarking methods are explored and compared: (1) the Most Likely-Landmark Locator (MLLL), based on maximizing the likelihood ratio [1], and (2) Viola-Jones detection [2]. Further, a landmark-correction method based on projection into a subspace is introduced. Both landmarking methods have been trained on the landmarked images in the BioID database [3]. The MLLL has been trained for locating 17 landmarks and the Viola-Jones method for 5 landmarks. The localization error and effects on the equal-error rate (EER) have been measured. In these experiments ground- truth data has been used as a reference. The results are described as follows:\ud
1. The localization errors obtained on the FRGC database are 4.2, 8.6 and 4.6 pixels for the Viola-Jones, the MLLL, and the MLLL after landmark correction, respectively. The inter-eye distance of the reference face is 100 pixels. The MLLL with landmark correction scores best in the verification experiment.\ud
2. Using more landmarks decreases the average localization error and the EER
A landmark paper in face recognition
Good registration (alignment to a reference) is essential for accurate face recognition. The effects of the number of landmarks on the mean localization error and the recognition performance are studied. Two landmarking methods are explored and compared for that purpose: (1) the most likely-landmark locator (MLLL), based on maximizing the likelihood ratio, and (2) Viola-Jones detection. Both use the locations of facial features (eyes, nose, mouth, etc) as landmarks. Further, a landmark-correction method (BILBO) based on projection into a subspace is introduced. The MLLL has been trained for locating 17 landmarks and the Viola-Jones method for 5. The mean localization errors and effects on the verification performance have been measured. It was found that on the eyes, the Viola-Jones detector is about 1% of the interocular distance more accurate than the MLLL-BILBO combination. On the nose and mouth, the MLLL-BILBO combination is about 0.5% of the inter-ocular distance more accurate than the Viola-Jones detector. Using more landmarks will result in lower equal-error rates, even when the landmarking is not so accurate. If the same landmarks are used, the most accurate landmarking method gives the best verification performance
Automatic landmark annotation and dense correspondence registration for 3D human facial images
Dense surface registration of three-dimensional (3D) human facial images
holds great potential for studies of human trait diversity, disease genetics,
and forensics. Non-rigid registration is particularly useful for establishing
dense anatomical correspondences between faces. Here we describe a novel
non-rigid registration method for fully automatic 3D facial image mapping. This
method comprises two steps: first, seventeen facial landmarks are automatically
annotated, mainly via PCA-based feature recognition following 3D-to-2D data
transformation. Second, an efficient thin-plate spline (TPS) protocol is used
to establish the dense anatomical correspondence between facial images, under
the guidance of the predefined landmarks. We demonstrate that this method is
robust and highly accurate, even for different ethnicities. The average face is
calculated for individuals of Han Chinese and Uyghur origins. While fully
automatic and computationally efficient, this method enables high-throughput
analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl
Fully automated landmarking and facial segmentation on 3D photographs
Three-dimensional facial stereophotogrammetry provides a detailed
representation of craniofacial soft tissue without the use of ionizing
radiation. While manual annotation of landmarks serves as the current gold
standard for cephalometric analysis, it is a time-consuming process and is
prone to human error. The aim in this study was to develop and evaluate an
automated cephalometric annotation method using a deep learning-based approach.
Ten landmarks were manually annotated on 2897 3D facial photographs by a single
observer. The automated landmarking workflow involved two successive
DiffusionNet models and additional algorithms for facial segmentation. The
dataset was randomly divided into a training and test dataset. The training
dataset was used to train the deep learning networks, whereas the test dataset
was used to evaluate the performance of the automated workflow. The precision
of the workflow was evaluated by calculating the Euclidean distances between
the automated and manual landmarks and compared to the intra-observer and
inter-observer variability of manual annotation and the semi-automated
landmarking method. The workflow was successful in 98.6% of all test cases. The
deep learning-based landmarking method achieved precise and consistent landmark
annotation. The mean precision of 1.69 (+/-1.15) mm was comparable to the
inter-observer variability (1.31 +/-0.91 mm) of manual annotation. The
Euclidean distance between the automated and manual landmarks was within 2 mm
in 69%. Automated landmark annotation on 3D photographs was achieved with the
DiffusionNet-based approach. The proposed method allows quantitative analysis
of large datasets and may be used in diagnosis, follow-up, and virtual surgical
planning.Comment: 13 pages, 4 figures, 7 tables, repository
https://github.com/rumc3dlab/3dlandmarkdetection
Registration of 3D Face Scans with Average Face Models
The accuracy of a 3D face recognition system depends on a correct registration that aligns the facial surfaces and makes a comparison possible. The best results obtained so far use a costly one-to-all registration approach, which requires the registration of each facial surface to all faces in the gallery. We explore the approach of registering the new facial surface to an average face model (AFM), which automatically establishes correspondence to the pre-registered gallery faces. We propose a new algorithm for constructing an AFM, and show that it works better than a recent approach. Extending the single-AFM approach, we propose to employ category-specific alternative AFMs for registration, and evaluate the effect on subsequent classification. We perform simulations with multiple AFMs that correspond to different clusters in the face shape space and compare these with gender and morphology based groupings. We show that the automatic clustering approach separates the faces into gender and morphology groups, consistent with the other race effect reported in the psychology literature. We inspect thin-plate spline and iterative closest point based registration schemes under manual or automatic landmark detection prior to registration. Finally, we describe and analyse a regular re-sampling method that significantly increases the accuracy of registration
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