Shape-appearance-correlated active appearance model

© 2016 Elsevier Ltd Among the challenges faced by current active shape or appearance models, facial-feature localization in the wild, with occlusion in a novel face image, i.e. in a generic environment, is regarded as one of the most difficult computer-vision tasks. In this paper, we propose an Active Appearance Model (AAM) to tackle the problem of generic environment. Firstly, a fast face-model initialization scheme is proposed, based on the idea that the local appearance of feature points can be accurately approximated with locality constraints. Nearest neighbors, which have similar poses and textures to a test face, are retrieved from a training set for constructing the initial face model. To further improve the fitting of the initial model to the test face, an orthogonal CCA (oCCA) is employed to increase the correlation between shape features and appearance features represented by Principal Component Analysis (PCA). With these two contributions, we propose a novel AAM, namely the shape-appearance-correlated AAM (SAC-AAM), and the optimization is solved by using the recently proposed fast simultaneous inverse compositional (Fast-SIC) algorithm. Experiment results demonstrate a 5–10% improvement on controlled and semi-controlled datasets, and with around 10% improvement on wild face datasets in terms of fitting accuracy compared to other state-of-the-art AAM models

Evaluation and Hardware Realization for a Face Recognition System

Facial recognition from an image or a video sequence draws attention for many image processing researchers owing to its myriad applications in real world as well as in computer vision, human-computer interaction and intelligent systems. Facial structures have unique features which can be extracted using some mathematical tools. We have used Principal Component Analysis (PCA) and Local Binary Pattern (LBP) to extract them and stored them in a database. When the query image is given the facial features are extracted and compared to the previously obtained results using Sparse Face recognition. Detailed test methods have been defined and an extensive testing of the algorithm has been performed on various standard databases. The results have been tabulated with required graphs. The proposed algorithm has been compared to other different algorithms which show significant improvement in results with small number of training samples. Finally the algorithm was integrated in a hardware system so that it can be used as a self sufficient portable system

Face Alignment Using Texture-Constrained Active Shape Models

In this paper, we propose a texture-constrained active shape model (TC-ASM) to localize a face in an image. TC-ASM effectively incorporates not only the shape prior and local appearance around each landmark, but also the global texture constraint over the shape. Therefore, it performs stable to initialization, accurate in shape localization and robust to illumination variation, with low computational cost. Extensive experiments are provided to demonstrate our algorithm