## 1 Introduction  
Facial biometrics is widely extended nowadays because of its great number of applications, the maturity of the technology and the users acceptance. Typical uses include data, devices or facilities access control, surveillance, border crossing, entertainment and human-computer interaction. As might be expected, the variety of these application scenarios implies different needs regarding security, storage of information, computing capacity or samples collection. For instance, certain applications cannot store user's templates, are require to verify the identity of the user against a smart card (ID card, driving license or passport) or present difficulties to collect multiple samples of each user. Focussing on the availability of one or more samples during the training or the enrolment of the users into the system, applications can be divided in two groups, which are known as single sample per person (SSPP) and multiple samples per person (MSPP) problems. SSPP problem has received considerable attention during the last decades because of the relevance of its applications, particularly those related to security, surveillance and border crossing. Nowadays, it is increasingly popular between e-voting and bank of the future service providers. These applications need to guarantee the security of transactions but also to offer an attractive, useful and comfortable user experience, including the access from mobile devices, so they typically avoid to store any information about the user and compare a user's photograph and the image in his/her ID card. SSPP scenarios present lower costs of collecting, storing and processing samples but also add new challenges to typical face recognition difficulties (sensitivity to capturing conditions, facial expressions and changes in the appearance of the users among others). The reduction of the number of images implies a severe reduction on the recognition accuracy of most of the methods developed up to the moment, which strongly rely on the number of samples and their quality to generate a good facial model that generalises inter- and intra-person variability. Accordingly, a solution able to extract and generalise knowledge from previous experiences and similar environments is required. On the other hand, deep learning solutions for face recognition have received great attention during the last years. In fact, the introduction of convolutional neural networks (CNNs) for facial features extraction marked a turning point when Deep Face [1] and DeepID [2] were presented in 2014, as can be seen in the survey presented by [PERSON] and [PERSON] [3]. At the beginning, CNNs were mostly applied in MSPP scenarios, although they were recently also applied for SSPP problems achieving very promising results. Table 1 summarises the latest works on deep learning for face recognition on SSPP scenarios. Information about the dataset (name and main variations), the number of people and images used during the test, whether an additional dataset is used for training and the match rate are provided for each method to describe their performance. Most of these works provide an evaluation of the methods in terms of accuracy for identification scenarios. However, identity verification is a widely extended scenario. In addition, the specific requirements of the varied applications make necessary a complimentary evaluation of the architectures and the size of the models similarly to the work presented by [PERSON] et al. [4], which quantitatively compare the architectures of CNNs and evaluate the effect of different implementation choices using the public database LFW to train the models. Moreover, the use of public datasets for testing is crucial and the direct comparison against publicly available models seems to be necessary to state a base line.