Face Class Modeling based on Local Appearance for Recognition

International audienceThis work proposes a new formulation of the objects modeling combining geometry and appearance. The object local appearance location is referenced with respect to an invariant which is a geometric landmark. The appearance (shape and texture) is a combination of Harris-Laplace descriptor and local binary pattern (LBP), all is described by the invariant local appearance model (ILAM). We applied the model to describe and learn facial appearances and to recognize them. Given the extracted visual traits from a test image, ILAM model is performed to predict the most similar features to the facial appearance, first, by estimating the highest facial probability, then in terms of LBP Histogram-based measure. Finally, by a geometric computing the invariant allows to locate appearance in the image. We evaluate the model by testing it on different images databases. The experiments show that the model results in high accuracy of detection and provides an acceptable tolerance to the appearance variability

Viewpoint Invariant Face Detection

International audienceIn this paper we present a face model based on learning a relation between local features and a face invariant. We have developed a face invariant model for accurate face localization in natural images that is robust to viewpoints changes. A probabilistic model learned from a training set captures a relationship between features appearance and face invariant geometry. It is then used to infer a face instance in new images. We use the invariant local features which have high performances for objects appearance distinctiveness. The face appearance features are recognized by EM classification. Then, face invariant parameters are predicted and a hierarchical clustering method achieves invariant geometric localization. The face appearance probabilities of features are computed to select the best clusters and thus to localize faces in images. We evaluate our generic invariant by testing it in face detection experiments on PIE, FERET and CMU-Profiles databases. The experimental results show that our face invariant model gives highly accurate face localization