The human face is perhaps the most familiar and easily recognized object in the world, yet both its three-dimensional shape and its two-dimensional images are complex and hard to characterize. This book develops the vocabulary of ridges and parabolic curves, of illumination eigenfaces and elastic warpings for describing the perceptually salient features of a face and its images. The book also explores the underlying mathematics and applies these mathematical techniques to the computer vision problem of face recognition, using both optical and range images.

Two- and Three-Dimensional Patterns of the Face

Objectives:To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR).Methods:Studies using application...

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The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals, and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopeneaus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large-scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer solutions to the genotyping by sequencing approach, and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection, through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps which impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short and long-term solutions to sustainable and profitable aquaculture industries.

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Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters.

We address the problem of automatically detecting a sparse set of 3D mesh vertices, likely to be good candidates for determining correspondences, even on soft organic objects. We focus on 3D face scans, on which single local shape descriptor responses are known to be weak, sparse or noisy. Our machine-learning approach consists of computing feature vectors containing $$D$$ different local surface descriptors. These vectors are normalized with respect to the learned distribution of those descriptors for some given target shape (landmark) of interest. Then, an optimal function of this vector is extracted that best separates this particular target shape from its surrounding region within the set of training data. We investigate two alternatives for this optimal function: a linear method, namely Linear Discriminant Analysis, and a non-linear method, namely AdaBoost. We evaluate our approach by landmarking 3D face scans in the FRGC v2 and Bosphorus 3D face datasets. Our system achieves state-of-the-art performance while being highly generic.

A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes

Face landmarking, defined as the detection and localization of certain characteristic points on the face, is an important intermediary step for many subsequent face processing operations that range from biometric recognition to the understanding of mental states. Despite its conceptual simplicity, this computer vision problem has proven extremely challenging due to inherent face variability as well as the multitude of confounding factors such as pose, expression, illumination and occlusions. The purpose of this survey is to give an overview of landmarking algorithms and their progress over the last decade, categorize them and show comparative performance statistics of the state of the art. We discuss the main trends and indicate current shortcomings with the expectation that this survey will provide further impetus for the much needed high-performance, real-life face landmarking operating at video rates.

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A comparative study of face landmarking techniques

Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections.

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data).

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From 3D Point Clouds to Pose-Normalised Depth Maps

Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes

A prototype to measure rainbow trout's length using image processing

A comparison of several approaches that use graph matching and cascade filtering for landmark localization in 3D face data is presented. For the first method, we apply the structural graph matching algorithm “relaxation by elimination” using a simple “distance to local plane” node property and a “Euclidean distance” arc property. After the graph matching process has eliminated unlikely candidates, the most likely triplet is selected, by exhaustive search, as the minimum Mahalanobis distance over a six dimensional space, corresponding to three node variables and three arc variables. A second method uses state-of-the-art pose-invariant feature descriptors embedded into a cascade filter to localize the nose tip. After that, local graph matching is applied to localize the inner eye corners. We evaluate our systems by computing root mean square errors of estimated landmark locations against ground truth landmark localizations within the 3D Face Recognition Grand Challenge database. Our best system, which uses a novel pose-invariant shape descriptor, scores 99.77% successful localization of the nose and 96.82% successful localization of the eyes.

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Marcelo Romero

Papers

Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections.

From 3D Point Clouds to Pose-Normalised Depth Maps

Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes

A prototype to measure rainbow trout's length using image processing

Landmark Localisation in 3D Face Data