# Towards Facial Biometrics for ID Document Validation in Mobile Devices
## 2 Related Work
### Face Recognition with Machine Learning  
Modern face recognition intensively uses recent achievements in machine learning. These tools are usually served to learn the discrimination of face descriptors, for example by solving a classification task to estimate the similarity between images. The face representation in these approaches is usually engineered and based on low-level face image information [10; 13].  
Another approach to face recognition came with convolutional deep networks which give the ability to efficiently learn discriminative face features themselves even from unconstrained images. These learned features generally do not include local image characteristics and realize the face description in a high-level manner [8]. Significant popularity in face recognition systems was gained by metric learning approaches which are focused on straightforwardly optimizing the face representation. For example, [PERSON] et al. [18] introduced a triplet loss for face recognition which minimizes the distance in the feature domain between samples of the same identity and maximizes it for the disjoint identities. However, such methods usually require unreasonably large datasets accompanied by a carefully designed sample mining strategy for successful convergence.  
The best performance in unconstrained face recognition is achieved by methods that consider the problem as a classification task separating images by their identities. These approaches usually utilize softmax-based classification loss [8] which allows learning the discriminative face features, which may be further used for distinguishing tasks with trivial similarity metrics. Nowadays, investigation is focused on modifying softmax loss by different means. The main purpose of most of the published improvements is enlarging the inter-class variance and reducing the inter-class compactness [19]. For example, particular attention was paid to constraining the target feature distribution with a margin of a different kind [20,21].  
Some deep learning methods also find their application in face recognition systems for document security. As an example, [PERSON] et al. [22] proposed a method for 1-1 authentication for the differential automatic border control scenario. In their system, the face photo on the ID document is validated with the help of life face capture. The two images are processed by separate networks to estimate the similarity of their deep representations.  
However, the intricacy and the lack of a clear sense of extracted deep representations may be an obstacle in practice while embedding these approaches in portable devices. The computational complexity requirements of deep learning approaches are still high for most of the smartphones on the market.  
At the same time, the differential manner of match-on-document scenario implies 1-1 verification of templates extracted from an original digital image and its copy, which is printed on the ID document. With such a scenario, the engineered features, which may catch characteristic peculiarities of the particular image, have better usage perspectives than learned high-level features. However, machine learning tools indeed may be applied for distinguishing such engineered face feature representations.