Multimodal 2D-3D face recognition

Up to date, many advances have been made to 2D face recognition (2D FR) due to its broad range of applications in security and commercial areas as well as in smart devices. However, 2D FR is still quite vulnerable under unconstrained conditions of the image acquisition process. To overcome 2D FR limitations, researchers shift to 3D face recognition technology but this technology is computationally expensive and inapplicable to real-world face recognition systems. Multimodal 2D-3D face recognition can combine the strength of both 2D and 3D modalities. In this paper a multimodal 2D-3D face recognition approach has been proposed based on geometric and textural characteristics of 2D and 3D modalities. The conducted experiments show that the proposed approach achieved promising results with illumination and head pose variations. The performance is evaluated using the landmark Bosphorus facial database

Region-based facial expression recognition in still images

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach