# A Multicomponent Face Verification and Identification System
## 1 Introduction
### Related Work  
A typical face recognition system broadly follows three steps: (i) detection and extraction/capture of face, (ii) feature extraction and representation, and (iii) face recognition [5]. Related to the feature extraction, for instance, FaceNet creates unified embeddings of the faces and then compares the faces in the embedding space [46]. FaceNet is a DNN used to obtain facial features and determine if there is a match between the input faces and a non-matching face with triplet-based loss function. More details for the feature extraction of FaceNet can be found in [8; 23; 47]. In [48] the authors focused on building a secure authentication system with face, location, and gesture recognition as components. User gestures and location data were a sequence of time series. They utilized unsupervised learning in a long short-term memory recurrent neural network to actively learn to recognize, group, and discriminate user gestures and location. Moreover, a clustering-based technique was also implemented for recognizing gestures and location. In [49] the authors presented a fast and invariant to illumination changes AdaBoost framework for face and eye detection. The main components of the proposed architecture were face detection at a distance, face feature extraction, user ID recognition, and user authenticator. In [50] a face detection and recognition system for blind people in real time by using the Raspberry Pi and Android app was introduced. The main aspects of the study were an object detection function based on boosted cascade and a face recognition function based on eigenfaces. In [51] the authors present the architecture of a smart imaging sensor system for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain and a digital coprocessor that performs image classification. In [52] a system was designed for the identification of person's face with or without a mask. The input data were obtained from either an image or a video adopting in the architecture used a MobileNet. In [53], the authors proposed a system with several features such as face database construction, database management system, and human-computer interaction system function. Their system was based on a CNN. In [54], an encrypted face recognition payment system was proposed. In [55], two deep learning models for face recognition were specially designed for applications on mobile devices and resources saving environments. In [56] the authors introduced an innovative real-time framework that leverages deep learning techniques, particularly CNNs, to accurately detect human faces in complex images. In [57], a novel approach to real-time criminal detection through the use of cutting-edge face recognition technology was proposed. The proposed system uses a multi-task cascade neural network to reliably identify and recognize faces in difficult situations, such as low light or obscured views. The authors of [58] proposed a facial recognition intrusion detection system in energy-efficient terms. As referenced in [55; 59], nowadays, the future solutions and research include the design and development of mobile, ubiquitous portable, and low resource devices for face recognition in various research fields.