Three dimensional face recognition based on geodesic and Euclidean distances

External ventricular drains (EVD) are widely used to measure and manage intracranial pressure (ICP) for aneurysmal subarachnoid hemorrhage (aSAH) patients. After several days of use a decision is made to remove the drain or replace it with an indwelling shunt. This involves a “clamping trial” whereby the EVD is clamped and transduced and pre and post clamping CT imaging is performed to observe any change in ventricular size and clinical status. This practice may lead to prolonged hospital stay, extra radiation exposure, and neurological insult. The present study aims to apply a widely validated morphological clustering analysis of ICP pulse (MOCAIP) algorithm to detect signatures from the pulse waveform to differentiate an intact CSF circulatory system from an abnormal one during EVD clamping. We hypothesize that an intact CSF system should be stable and pulses with a similar mean ICP level should have similar shapes. This is a retrospective study with 43 out of 107 adult patients that satisfied the inclusion criteria of ICD9 code for SAH, admitted to UCSF Medical Center between 03/2013 and 08/2014, and exclusion criteria of absence of reported EVD clamping trial and ICP signal recordings shorter than 2 hours. By reviewing the clinical notes and pre/post brain imaging results, 25 patients were determined with an intact CSF circulatory system (group A) and 18 patients with an impaired one (group B). Blind to the chart review, the MOCAIP algorithm was applied to the ICP signal recordings to form a series of artifact-free dominant pulses. Then, for each pulse, other pulses with similar mean ICP were identified. Finally, the Euclidean and geodesic inter-pulse distances were calculated in a 4 hours moving window. ANOVA analyses showed a significant difference on the standard deviations of the Euclidean (p<0.001, η2=0.29) and geodesic distance (p=0.001, η2=0.22), between groups A and B. Mann-Whitney U tests were used on the (non-normally distributed) mean distances, showing a significant difference on the Euclidean (p=0.007, r=0.41) and the geodesic distance (p=0.02, r=0.36) between the two groups of patients. The area under the ROC curve was 0.79 and 0.78 for the standard deviations of the Euclidean and geodesic distance, and 0.74 and 0.71 for the mean Euclidean and geodesic distance respectively. Patients with an impaired CSF system showed not only a larger mean but also a larger variability of the inter-pulse distances, indicating frequent changes on the morphology of the pulses. The variability of morphological distances was the best predictor of the effect of EVD clamping trial. This technique may provide a method to rapidly determine if a patient will need surgical placement of an indwelling shunt or can simply have the EVD removed following SAH.

Compared with 2D face recognition systems, 3D facial recognition has been a popular area of study in academia and industry, because of its ability to detect human faces in an unconstrained environment more efficiently. This study presents a new 3D face identification method that uses 3D facial images to compute geodesic distances using 3D landmarks. Feature extraction is computationally more efficient than Euclidean distance using geodesic distance. This study aims to demonstrate how to differentiate identical twins based solely on facial features by developing an efficient algorithm for calculating geodesic paths and distances. We employed the A*, Dijkstra, and fast marching (FM) algorithms to calculate the geodesic distance. Quantitative similarity metrics are obtained and then utilized as inputs in numerous classification methods: random forest (RF), extra tree classifier (ETC), light gradient boosting machine (LGBM), support vector machine (SVM), and bagging classifiers. An expression challenge dataset for 3D twins (3D-TEC), the most challenging data set for 3D face recognition research at Notre Dame University, was used to validate and test the performance of these algorithms. The recall, accuracy, precision, training time, and F1 score were compared for each classifier to determine the best performance. The results from the experiment indicate that, when compared to other algorithms and classification models, the Extra Tree Classifier is the most accurate (by 90%) in predicting face images, whereas A* is the best technique for computing geodesic distance.

Normal functioning of the brain depends on the homeostasis (∼ steady state) of its various physiological sub-systems, one of which is the intracranial pressure (ICP) dynamic system. The ICP dynamic system of an injured brain is susceptible to various acute changes that should ideally be detected by ICP monitoring even for comatose patients. However, the status quo of ICP monitoring solely targets mean ICP. We aimed to demonstrate a novel approach to detect acute deviation from steady state of an ICP dynamic system in an absence of significant mean ICP changes. We hypothesized that steady state of ICP dynamic systems is reflected as ICP pulses of similar mean ICP levels resembling each other for a given subject. A general framework was used to derive such a steady-state indicator that can accommodate different metrics of inter-pulse distance and different statistics of the distance histograms. In addition to conventional Euclidean distance and Pearson correlation, geodesic distance between pulses was introduced as a novel metric. These different ways of calculating steady-state indicators under the proposed framework were evaluated on three types of continuous ICP recordings: (1) those between two consecutive brain imaging studies that demonstrated acute ventricular enlargement for slit ventricle syndrome (SVS) patients undergoing a trial of shunt externalization and clamping (SVS+); (2) those between consecutive brain imaging studies from the SVS patients under the same trial but without ventricular enlargement (SVS−); (3) overnight recordings from normal pressure hydrocephalus (NPH) patients. It was observed that only the standard deviation of geodesic distance correctly differentiated between SVS+ and SVS− and between SVS+ and NPH while avoiding discriminating between SVS− and NPH. It was also found that 45% SVS+ cases had a multimodal geodesic distance histogram while none of SVS− and 3.8% of NPH cases had such a multimodal histogram. Pulses with a large number of distant pulses for the five multimodal-histogram SVS+ cases fell in short time windows indicating that acute ventricular changes may have occurred in these confined time windows during which no significant changes of mean ICP were observed. In contrast, the pulses with a large number of distant pulses for the two multimodal-histogram NPH cases did not cluster temporally. In conclusion, the geodesic inter-pulse distance is a promising metric to quantify distance intrinsic to the underneath geometric structure of ICP signals and hence is a more suitable way to derive a steady-state indicator of an ICP dynamic system.

Mathematical morphology can be used to extract a shape-size distribution called pattern spectrum (PS) with texture description purposes. However, the structuring element (SE) used to compute it does not vary along the image; and therefore it does not capture its geometrical variations. The author-s proposal consists of computing an SE at each pixel whose size and shape varies with two distance criterions: an Geodesic distance and a Euclidean distance, in order to fit the texture as well as possible. Combining the Geodesic and the Euclidean descriptors as just one descriptor, the classification results of several textures from the VisTex and Brodatz database show that this approach outperforms the classical PS, the Geodesic and the Euclidean descriptors separately and, in contrast with other adaptive methods, it does not require previous training.

Face recognition research mainly focuses on traditional 2D color images, which is extremely susceptible to be affected by external factors such as various viewpoints and has limited recognition accuracy. In order to achieve improved recognition performance, as well as the 3D face holds more abundant information than 2D, we present a 3D human face recognition algorithm using the Microsoft's Kinect. The proposed approach integrates the depth data with the RGB data to generate 3D face raw data and then extracts feature points, identifies the target via a two-level cascade classifier. Also, we build a 3D-face database including 16 individuals captured exclusively using Kinect. The experimental results indicate that the introduced algorithm can not only achieve better recognition accuracy in comparison to existing 2D and 3D face recognition algorithms when the probe face is exactly in front of Kinect sensor, but also can increase 9.3% of recognition accuracy compared to the PCA-3D algorithm when it is not confronting the camera.

Research was conducted to investigate the use of optical, air-born acoustic and plasma charge signals for detecting defects in laser welds. Full penetration, partial penetration and gapped-full penetration butt-joint welds were made in 1.4mm cold-rolled steel sheet. A 3 kW CO2 laser was used to produce these three classes of welds while laser power, travel speed and joint fitup were varied to produce the various conditions. Visible light, audible sound and electrical charge near the plume were measured simultaneously in real-time using a silicon photodiode, a condensor microphone and a plasma charge sensing gas nozzle, respectively. The spectra of the sensor signals were analyzed by a statistical technique known as Linear Discriminant Analysis (LDA). Stepwise LDA was used to identify the most important frequency bands and canonical discriminant analysis was employed to develop discriminant functions. Reliability analysis of the training samples revealed that this LDA method classified laser welds with reliability ranging from 85% to 96%, depending on the welds selected for inclusion in the training set. When the linear discriminant function was validated using new (untrained) samples, it distinguished the three different classes of laser welds with approximately 87% accuracy. Among the three signals, sound produced better separation between the classes of welds, although a discriminator based on all three signals combined and one that used only plasma charge and acoustic signals were of almost equal reliability.Research was conducted to investigate the use of optical, air-born acoustic and plasma charge signals for detecting defects in laser welds. Full penetration, partial penetration and gapped-full penetration butt-joint welds were made in 1.4mm cold-rolled steel sheet. A 3 kW CO2 laser was used to produce these three classes of welds while laser power, travel speed and joint fitup were varied to produce the various conditions. Visible light, audible sound and electrical charge near the plume were measured simultaneously in real-time using a silicon photodiode, a condensor microphone and a plasma charge sensing gas nozzle, respectively. The spectra of the sensor signals were analyzed by a statistical technique known as Linear Discriminant Analysis (LDA). Stepwise LDA was used to identify the most important frequency bands and canonical discriminant analysis was employed to develop discriminant functions. Reliability analysis of the training samples revealed that this LDA method classified laser welds with rel...

Fourier descriptor is widely used for shape analysis and shape matching. Generally, the Euclid distance from boundary point to shape centroid is used in constructing Fourier descriptor. This kind of shape descriptor, however, is sensitive for rigid-transform. In this paper, we proposed a new kind of shape descriptor, namely geodesic Fourier descriptor. It remains robust under rigid transform. We first define a reference point by Poisson equation, which remains almost invariant under rigid transform. Then, the geodesic distance from shape boundary to reference point is used to construct GFD. Geodesic distance shows distinct advantage over the Euclid distance due to its robustness under rigid transformation. An algorithm based on two-scan dilating operation is presented to compute the geodesic distance efficiently in discrete image fields. Finally, experiments are carried out to show that geodesic Fourier descriptor can achieve better matching precision than Euclid distance based Fourier descriptor.

The purpose of this study is to enhance the algorithms towards the development of an efficient three dimensional face recognition system in the presence of expressions. The overall aim is to analyse patterns of expressions based on techniques relating to feature distances compare to the benchmarks. To investigate how the use of distances can help the recognition process, a feature set of diagonal distance patterns, were determined and extracted to distinguish face models. The significant finding is that, to solve the problem arising from data with facial expressions, the feature sets of the expression-invariant and expression-variant regions were determined and described by geodesic distances and Euclidean distances. By using regression models, the correlations between expressions and neutral feature sets were identified. The results of the study have indicated that our proposed analysis methods of facial expressions, was capable of undertaking face recognition using a minimum set of features improving efficiency and computation.

Many recent ultrasound image processing methods are based on patch comparison, such as filtering and segmentation. Identifying similar patches in noise-corrupted images is a key factor for the performance of these methods. While the Euclidean distance is ideal to handle the patch comparison under additive Gaussian noise, finding good measures to compare patches corrupted by multiplicative noise is still an open research. In this paper, we deduce several new geodesic distances, arising from parametric probabilistic spaces, and suggest them as similarity measures to process RF and log-compressed ultrasound images in patch-based methods. We provide practical examples using these measures in the fields of ultrasound image filtering and segmentation, with results that confirm the potential of the technique.

PurposePeople seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches.Design/methodology/approachThe authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches.FindingsBoth proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach.Research limitations/implicationsCurrent guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations.Originality/valueThis paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

An important task in brain connectivity research is the classification of patients from healthy subjects. In this work, we present a two-step mathematical framework allowing to discriminate between two groups of people with an application to multiple sclerosis. The proposed approach exploits the properties of the connectivity matrices determined using the covariances between signals of a fixed set of brain areas. These positive semidefinite matrices lay on a Riemannian manifold, allowing to use a geodesic distance defined on this space. In order to generate a vector representation useful for classification purposes, but still preserving the network structure, we encoded the data exploiting the network attractors determined by a geodesic clustering of connectivity matrices. Then clustering centroids were used as a dictionary allowing to encode subject's connectivity matrices as a vector of geodesic distances. A Linear Support Vector Machine was then used to perform classification between subjects. To demonstrate the advantage of using geodesic metrics in this framework, we conducted the same analysis using Euclidean metric. Experimental results validate the fact that employing geodesic metric in this framework leads to a higher classification performance, whereas performance with a Euclidean metric was sub-optimal.

Understanding the features employed by the human visual system in gender classification is considered a critical step towards improving machine based gender classification systems. We propose the use of 3D Euclidean and geodesic distances between biologically significant facial landmarks to classify gender. We perform five different experiments on the BU-3DFE face database to look for more representative features that can replicate our visual system. Based on our experiments we suggest that the human visual system looks at the ratio of 3D Euclidean and geodesic distance as these features can classify facial gender with an accuracy of 99.32%. The features selected by our proposed gender classification experiment are robust to ethnicity and moderate changes in expression. They also replicate the perceptual gender bias towards certain features and hence become good candidates for being a more representative feature set.

Pioneering work in cancer neuroscience revealed that glioma development and infiltration occur in part via novel, functional neuronal-tumor synapses. Resultant hyperexcitability, which perpetuates disease progression, can be detected using standard and high resolution electrophysiology techniques. However, differences across glioma subtypes and the topographic distribution of these alterations are not well characterized in existing literature. To interrogate these perturbations and their extent, we obtained intraoperative intracranial electrophysiology recordings in 72 patients (n=23 IDH-wildtype glioma, 15 IDH-mutant glioma, 28 non-tumor epilepsy cases, and 6 movement disorder cases) using standard (e.g., 6-8 contacts) clinical electrodes (ECoG, n=40), microelectrodes with 128-1024 channels that conform to the brain surface (n=46), and Neuropixels probes (n=29). All recordings were from the frontal, temporal, or parietal lobe. Electrode location relative to the enhancing tumor and associated T2 hyperintensity was determined using a 3D localization pipeline, coregistered to segmented pre-operative imaging. Endpoints include spectral power band analysis with modeling of periodic and aperiodic components and interictal discharge rates (IIDs). Euclidean and geodesic distance, with the latter following anatomic geography, to the enhancing tumor boundary was calculated per participant and channel. Clinical ECoG grids did not discriminate regional or overall differences in high gamma power (HGP), aperiodic (E/I) slope or exponent, or IIDs. However, using high resolution grids, increasing geodesic distance was associated with a linear decrease in high gamma power for both IDH-mutant and -wildtype glioma cases. Similarly, the E/I slope of 30-50 Hz activity increased linearly with distance from tumor boundary, implicating a relative shift toward peritumoral excitatory activity. IDH-wildtype glioma was associated with the highest HGP and IID rate, and geodesic distance was most precise in distinguishing region differences; both distance calculations were relevant for IDH-mutant tumors. Comparison to non-tumor cases highlighted mechanistic similarities across neurologic diseases that may be identifiable using neuroelectrical signatures.

Nonlinear data projection based on geodesic distances requires the construction of a neighborhood graph that spans all data points so that the geodesic distance between any pair of data points could be estimated by the graph distance between the pair. This paper proposes an approach for constructing a k-edge connected neighborhood graph. The approach works by repeatedly extracting minimum spanning trees from the complete Euclidean graph of all data points. The constructed neighborhood graph has the following properties: (1) it is k-connected; (2) each point connects to its k-nearest neighbors; (3) if the graph is cut into two partitions, the cut edges contain k-shortest edges between the two partitions. Experiments show that the presented approach works well for clustered data and outperforms the nearest neighbor approaches used in Isomap for evenly distributed data.

Density peaks clustering (DPC) is a density-based clustering algorithm with excellent clustering performance including accuracy, automatically detecting the number of clusters, and identifying center points. However, DPC has some shortcomings to be addressed before it can be widely applied. For example, sensitive predefined parameter is not suitable for manifold datasets, and decision graph easily causes the wrong center points. To address these issues, a new DPC algorithm based on weighted k-nearest neighbors and geodesic distance (DPC-WKNN-GD) is proposed in this article to improve the clustering performance for manifold and non-manifold datasets. The DPC-WKNN-GD introduces the weighted k-nearest neighbors based on Euclidean distance to optimize the local density <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> . Inspired by DPC-GD, the DPC-WKNN-GD redefines the distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> based on geodesic distance. The experimental results on artificial and real-world datasets, including image datasets, show that the DPC-WKNN-GD outperforms the state-of-the-art comparison algorithms on both manifold and non-manifold datasets. In addition, the DPC-WKNN-GD completely overcomes the DPC-GD defect, in which the decision graph displays misleading center points.