## 1 Introduction  
### Geometric Morphometrics  
Geometric Morphometrics (GM) is a powerful multivariate statistical toolset for the analysis of morphology [1]. These methods are of a growing importance in fields such as biology and physical anthropology, with many implications for evolutionary theory and systematics. GM applications employ the use of two or three dimensional homologous points of interest, known as _landmarks_, to quantify geometric variances among individuals [1; 2; 3; 4]. GM practices first project landmark configurations onto a common coordinate system. This process is carried out via a series of superimposition procedures, including scaling, rotation and translation, frequently known as Generalized Procrustes Analyses (GPA). GPA is a powerful technique that allows for the direct comparison of landmark configurations, quantifying minute displacements of individual landmarks in space [5; 6]. These distortions and deformations can then be used to highlight geometric variations among organisms and can be visualized with ease. From these superimposed configurations, matrix operations from linear algebra can be performed to project each element under study as a single multidimensional (\(\mathbb{R}^{n}\)) point in a newly constructed feature space. This procedure, known as Principal Components Analysis (PCA) is useful for dimensionality reduction and converting landmarks into more manageable data for complex statistical applications [7; 8]. A wide array of techniques are known for different pattern recognition and classification tasks in GM. From one perspective, more traditional parametric and non-parametric multivariate statistical analyses can be performed to assess differences and similarities among sample distributions [7]. Likewise, generalized distances and group association probabilities can be used to compare groups of organisms and trends in variation and covariation [9]. Moreover, many popular classification tasks rely on parametric discriminant functions [10; 11]. In more recent years, tasks in pattern recognition and classification have received an increase in efficiency and precision with the implementation of Artificially Intelligent Algorithms (AIAs), reporting \(>\)90% accuracy in GM applications. In a broad sense, AIAs are algorithms designed to \"learn\" from data so as to perform a wide array of different tasks. In this context, AIAs can be programmed to automatically learn from subsets of data to adjust their internal parameters, while using other subsets to validate these parameters for performing a certain task [12]. Among the multitude of available algorithms, the most popular AIAs for classification purposes in GM currently include Support Vector Machines (SVM) [13; 14; 15; 16], and Artificial Neural Networks (ANN) [17; 18; 19; 20; 21]. Both algorithms present distinct advantages, especially in the processing of complex high-dimensional data. As opposed to traditional Linear and/or Partial Least-Squares Discriminant Analyses (LDA and PLSDA), SVMs and ANNs are less susceptible to underlying assumptions within model properties. SVMs, for example, are able to use numerous different kernel functions to overcome issues imposed by linearity [22; 23]. ANNs, on the other hand, are highly versatile non-linear algorithms inspired by information processing in the brain, achieving above human performance in a multitude of real-life situations [23; 24; 25]. Nevertheless, each of these types of analyses are susceptible to a number of different problems, all of which can affect the reliability of the extracted data. From one perspective, numerous studies have focused on the error produced through data collection procedures, whether this type of error be induced by analyst experience, collection protocols or the definition of the landmark itself [26; 27; 28; 29]. Landmarks, for example, can be divided into several different types.