# A Multicomponent Face Verification and Identification System
## 1 Introduction  
Face recognition technology is a biometric technology, which is based on the identification or verification of facial features [1, 2, 3]. Automatic face recognition in 2D and 3D [4] is an active research field in computer vision tasks and artificial intelligence (AI) that is fundamental for a variety of real-time applications. Face recognition can be performed either by verification or identification. Verification involves comparison of the features related to a given face with the corresponding feature template against a claimed identity mainly presented in a document or card. It involves only one single comparison. On the other side, identification compares the given face with all the feature templates stored in a database to find the identity of a face among several possibilities that matches the most closely. Face identification takes time proportional to the size of the database [5].  
Related to the face recognition accuracy, there are several challenges due to pose variation, facial expression scale variation, aging, partial occlusion, resolution, noise or blur, and complex illumination [5; 6; 7; 8]. Deep learning (DL) techniques have received increased attention for achieving satisfying results in many problems related to feature extraction, object detection, classification, semantic segmentation, and other applications by implementing deep neural network (DNN) schemes under a supervised or an unsupervised setting [9; 10; 11; 12; 13]. In this content, several DNNs algorithms have been developed in recent years to achieve high accuracy and performance on face recognition tasks [14; 15]. Despite the complexity of such architectures and the high computational costs, they provide several advantages, compared with traditional methods, in terms of learning ability, high variability, and generalization [5; 16]. Among DNNs, convolutional neural networks (CNNs) are considered the best-fitting option [17; 18]. Such well-known and efficient DNN and CNN models are (i) the DeepFace [19], (ii) DeepID [20], (iii) DeepID2 [21], (iv) Baidu [22], (v) FaceNet [23], (vi) VGGFace [24], (vii) LightCNN [25], (viii) CNN with spatial transformer layers [26], (ix) deep coupled ResNet (DCR) [27], (x) PSI-CNN [28], (xi) ArcFace [29], (xii) MagFace [30], and (xiii) CFR-GAN [31]. The aforementioned models require a huge amount of memory resulting in limited devices and embedded systems. Despite the rise of several lightweight CNN architectures such as PocketNet [32], MobileFaceNet [33], EfficientNet-B0 [34], and GhostNet [35], it seems that they suffer from high floating point operations [36], as well as their heavy and more complex counterparts, such as face rotation and low-level face inputs [17].  
In [5; 8; 14; 17; 37; 38; 39; 40; 41], a number of face datasets are referenced. In [14; 42; 43], several innovative approaches are presented such as explainable techniques [44] or the development of semi-supervised methods exploiting little labeled data, as well as the utilization of transfer learning and curriculum learning methods to enable the extension of existing methods to new data instead of starting from scratch every time. Furthermore, the authors highlight the necessity of the use of proper diversity datasets, such as [39], to reduce gender and racial bias in the networks.  
Finally, related to the related legal framework in Europe for the use of AI systems, the AI Act (Regulation (EU) 2024/1689 laying down harmonized rules on artificial intelligence) has been implemented. The aim of the rules is to foster trustworthy AI in Europe. The AI Act sets out a clear set of risk-based rules for AI developers and deployers regarding specific uses of AI including, among others, biometric systems. More details for the AI Act can be found in [45].