## 2 Literature Overview  
Face recognition approaches have relied on the use of a wide variety of feature extraction algorithms such as: Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), Principal Component Analysis (PCA), Gabor filters, and frequency domain analysis, among many others. These features are used to train Machine Learning models or are matched with functions that measure their similarity or geometric distance to achieve high performance [1; 12; 13]. State of the art approaches have focused on extracting image featuresusing Deep Learning Networks to improve recognition performance due to the parameter optimization at training time. Various architectures trained for image classification have been adapted to perform face recognition and have obtained outstanding performances. In Ref. [14] the authors train a Deep Convolutional Neural Network (DCNN) with 5 layers for face recognition and make a comparison of performance and training time using the pre-trained architectures AlexNet with 8 layers and GoogleNet with 22 layers. Their results yield an accuracy of 100% for all networks with the face94 database, though pre-trained models achieved these results with less training iterations than their proposed DCNN. They tested their networks with noisy samples and studied how the accuracy decreased according to a parameter that controlled the noise level. [PERSON] et al. [15] conducted a study on the impact of different hyperparameter values on the accuracy of various Residual Networks trained and tested with a dataset that contains faces in different positions and orientations. A certain set of hyperparameters allow a particular network to achieve a 99% accuracy. Another popular network is FaceNet; in Ref. [16] a pre-trained model was fine-tuned with CASIA-Webface database and tested with other databases. In most of their cases an accuracy higher than 99% was achieved. In Ref. [17] a pre-trained DenseNet architecture is trained with the Wider Face dataset, obtaining results above 90% in most cases. Additional reports on face recognition approaches with pre-trained architectures can be found in Refs. [13; 18]. In Ref. [19] LBP and CNN extracted features are fused using an adaptive weighting function. [PERSON] et al. [20] proposed a set of image transformations to increase the amount of face images to train a CNN to recognize class attending students in a picture. Speaker recognition (voice or speech biometric modality) has been performed with popular approaches such as Gaussian Mixture Models (GMM) or Hidden Markov Models (HMM) whose parameters have been tuned with features based on Mel Frequency Cepstral Coefficients (MFCC). These approaches are combined with Universal Background Models (UBM) in order to calculate the log likelihood ratio to perform a hypothesis test [21]. A breakthrough in speaker verification came with the development of the i-vectors approach presented in Ref. [22]. The i-vector framework has also been combined with other models to improve verification results, most commonly using HMM and GMM [21; 23]. Early work on Deep Learning based speaker recognition proposed the use of bottleneck features extracted from the middle layer of a Deep Neural Network (DNN) [23]. [PERSON] et al. [24] created a CNN to extract deep embeddings to perform a text prompted verification task on Part 3 of the KSR2015 database using a 5 digit password. Their results show EER values lower than 10%. In recent years, X-vectors have become a widely used embedding due to the outstanding performance achieved [25]. In this framework, a DNN is trained to map variable length utterances to fixed length embeddings using a training scheme based on bank filtered features. X-vectors embeddings achieved EER values as low as 5.71 when they were evaluated on Speakers in the Wild (SITW) dataset and the Cantonese portion of NIST SRE 2016. Recent approaches such as [PERSON] et al. [26] and [PERSON] et al.