# Towards Facial Biometrics for ID Document Validation in Mobile Devices
## 2 Related Work
### AAM in Face Recognition  
Significant improvements in engineered methods for face recognition were achieved with the development of various techniques for automatic detection of special landmarks that allow localizing semantic regions on the face image. The list of selected face features is usually included in the standardized active appearance model (AAM) [9]. Such an approach gives huge opportunities for analyzing the face structure and processing the raw face images which have become very useful for face recognition applications.  
For example, [PERSON] et al. [10] used facial features extracted with the use of AAM for the purposes of face verification that was performed with the trained classifier. [PERSON] et al. [11] analyzed geometric face distances and characteristics of the semantic face shapes for face recognition purposes. The face recognition method reported by [PERSON] et al. [12] is based on face geometric invariances.  
Another approach to face recognition with the use of an active appearance model is based on detecting semantic regions and extracting local texture features for further analysis. For instance, [PERSON] et al. [13] considered both shape and texture information to discriminate face images. The face descriptor in this method is based on Local Binary Pattern (LBP) histograms extracted from the partitioned image. The dissimilarity metric between the descriptors is estimated by the nearest neighbor classifier.  
Many improvements were introduced to this technique. For instance, [PERSON] et al. [14] adopted discriminative LBP features for different color channels to enhance the performance for images with severe changes in illumination and face resolutions.  
Another popular technique for face recognition that deals with image texture is the histogram of oriented gradients (HOG) method, which usually implies sub-sampling images to small blocks and counting proportions of gradient orientation in these localized segments of an image. The extracted coefficients may further serve as discriminative features of the image.  
The example of such an approach was reported by [PERSON] et al. [15] who analyzed different factors that affect the HOG-based face descriptor and the performance and computational efficiency in comparison with other texture-based techniques.  
[PERSON] et al. [16] considered the HOG descriptor with sub-sampling based on the facial landmarks. Various methods for increasing the robustness of extracted HOG features were considered by analyzing the impact of facial feature location error and replacing the detected features with the regular grid.  
Other texture analysis techniques are generally less popular but also attract the interest of face recognition research. The Gabor feature method is widely used in computer vision for pattern recognition tasks. Applying special Gabor filters, it is possible to extract the directivity and frequency of the content within the vicinity of some point or region. For example, [PERSON] et al. [17] used concatenated Gabor wavelet coefficients, which are extracted in the vicinity area of each detected facial landmark. Further, the descriptors are distinguished with a support vector machine classifier.  
In [5], the face recognition solution for the match-on-document scenario is introduced. It employs the process of encoding the biometric template into the secured MRC to be printed on the document. By comparison with this work, we revisit and improve the implementation of the facial biometric template, considering the methods of its compressing, which are important for the target platform (mobile devices).