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

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

Local appearance modeling for objects class recognition

International audienceIn this work, we propose a new formulation of the objects modeling combining geometry and appearance; it is useful for detection and recognition. 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 being described by the invariant local appearance model (ILAM). We use an improved variant of LBP traits at regions located by Harris–Laplace detector to encode local appearance. We applied the model to describe and learn object appearances (e.g., faces) and to recognize them. Given the extracted visual traits from a test image, ILAM model is carried out to predict the most similar features to the facial appearance: first, by estimating the highest facial probability and then in terms of LBP histogram-based measure, by computing the texture similarity. Finally, by a geometric calculation the invariant allows to locate an appearance in the image. We evaluate the model by testing it on different face images databases. The experiments show that the model results in high accuracy of detection and provides an acceptable tolerance to the appearance variability

Local appearance modeling for objects class recognition

International audienceIn this work, we propose a new formulation of the objects modeling combining geometry and appearance; it is useful for detection and recognition. 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 being described by the invariant local appearance model (ILAM). We use an improved variant of LBP traits at regions located by Harris–Laplace detector to encode local appearance. We applied the model to describe and learn object appearances (e.g., faces) and to recognize them. Given the extracted visual traits from a test image, ILAM model is carried out to predict the most similar features to the facial appearance: first, by estimating the highest facial probability and then in terms of LBP histogram-based measure, by computing the texture similarity. Finally, by a geometric calculation the invariant allows to locate an appearance in the image. We evaluate the model by testing it on different face images databases. The experiments show that the model results in high accuracy of detection and provides an acceptable tolerance to the appearance variability

Probabilistic Modeling for Face Detection and Gender Classification

International audienceIn this paper, we contribute to solve the simultaneous problems of face detection and gender classification from any viewpoint. We use an invariant model for accurate face localization based on a combination of appearance and geometry. A probabilistic matching of visual traits allows to classify the gender of face even when pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. This feature contributes to determine the gender of the face. We evaluate our model by testing it in experiments on different databases. The experimental results show that the face model performs well to detect face and gives a good gender recognition rate in the presence of viewpoint changes and facial appearance variability

Viewpoint Invariant Gender Recognition

International audienceIn this paper, we address a problem of gender classification of faces taken from arbitrary viewpoints. We use a face model for accurate face localization based on a combination of appearance and geometry. A probabilistic matching of particular traits on face allows to classify the gender of face even in case of important pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. Such feature contributes to determine the gender associated to a given face. We evaluate the model by testing it simultaneously in face localization and gender classification experiments on PIE, FERET and CMU-Profiles databases. The experimental results show that the probabilistic invariant model performs well to detect faces and gives a rate of 92.1% of accurate gender classification in the presence of viewpoint changes and large appearance variability of faces

Probabilistic Modeling for Face Detection and Gender Classification

International audienceIn this paper, we contribute to solve the simultaneous problems of face detection and gender classification from any viewpoint. We use an invariant model for accurate face localization based on a combination of appearance and geometry. A probabilistic matching of visual traits allows to classify the gender of face even when pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. This feature contributes to determine the gender of the face. We evaluate our model by testing it in experiments on different databases. The experimental results show that the face model performs well to detect face and gives a good gender recognition rate in the presence of viewpoint changes and facial appearance variability

Viewpoint Invariant Model for 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 a high performance 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 clustering uses an aggregate value to construct clusters of invariants. 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

Viewpoint Invariant Gender Recognition

International audienceIn this paper, we address a problem of gender classification of faces taken from arbitrary viewpoints. We use a face model for accurate face localization based on a combination of appearance and geometry. A probabilistic matching of particular traits on face allows to classify the gender of face even in case of important pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. Such feature contributes to determine the gender associated to a given face. We evaluate the model by testing it simultaneously in face localization and gender classification experiments on PIE, FERET and CMU-Profiles databases. The experimental results show that the probabilistic invariant model performs well to detect faces and gives a rate of 92.1% of accurate gender classification in the presence of viewpoint changes and large appearance variability of faces