Low Level Features for Quality Assessment of Facial Images

International audienceAn automated system that provides feedback about aesthetic quality of facial pictures could be of great interest for editing or selecting photos. Although image aesthetic quality assessment is a challenging task that requires understanding of subjective notions, the proposed work shows that facial image quality can be estimated by using low-level features only. This paper provides a method that can predict aesthetic quality scores of facial images. 15 features that depict technical aspects of images such as contrast, sharpness or colorfulness are computed on different image regions (face, eyes, mouth) and a machine learning algorithm is used to perform classification and scoring. Relevant features and facial image areas are selected by a feature ranking technique, increasing both classification and regression performance. Results are compared with recent works, and it is shown that by using the proposed low-level feature set, the best state of the art results are obtained

Fully automated facial picture evaluation using high level attributes

International audiencePeople automatically and quickly judge a facial picture from its appearance. Thus, developing tools that can reproduce human judgments may help consumers in their picture selection process. Previous work mostly studied the position of facial keypoints to make predictions about specific traits: trustworthiness, likability, competence, etc. In this work, high level attributes (e.g. gender, age, smile) are automatically extracted using 3 different tools and are used to build models adapted to each trait. Models are validated on a set of synthetic images and it is shown that using attributes increases significantly the correlation between human and algorithmic evaluations. Then, a new dataset of 140 images is presented and used to demonstrate the relevance of high level attributes for evaluating faces with respect to likability and competence. A model combining both facial keypoints and attributes is finally proposed and applied to picture selection: which picture depicts the most likable face for a given person

How to predict the global instantaneous feeling induced by a facial picture?

International audiencePicture selection is a time-consuming task for humans and a real challenge for machines, which have to retrieve complex and subjective information from image pixels. An automated system that infers human feelings from digital portraits would be of great help for profile picture selection, photo album creation or photo editing. In this work, two models of facial pictures evaluation are defined. The first one predicts the overall aesthetic quality of a facial image, and the second one answers the question " Among a set of facial pictures of a given person, on which picture does the person look like the most friendly? ". Aesthetic quality is evaluated by the computation of 15 features that encode low-level statistics in different image regions (face, eyes, mouth). Relevant features are automatically selected by a feature ranking technique, and the outputs of 4 learning algorithms are fused in order to make a robust and accurate prediction of the image quality. Results are compared with recent works and the proposed algorithm obtains the best performance. The same pipeline is considered to evaluate the likability of a facial picture, with the difference that the estimation is based on high-level attributes such as gender, age, smile. Performance of these attributes is compared with previous techniques that mostly rely on facial keypoints positions, and it is shown that it is possible to obtain likability predictions that are close to human perception. Finally, a combination of both models that selects a likable facial image of good quality for a given person is described

Estimation automatique des impressions véhiculées par une photographie de visage

Picture selection is a time-consuming task for humans and a real challenge for machines, which have to retrieve complex and subjective information from image pixels. An automated system that infers human feelings from digital portraits would be of great help for profile picture selection, photo album creation or photo editing. In this work, several models of facial pictures evaluation are defined. The first one predicts the overall aesthetic quality of a facial image by computing 15 features that encode low-level statistics in different image regions (face, eyes and mouth). Relevant features are automatically selected by a feature ranking technique, and the outputs of 4 learning algorithms are fused in order to make a robust and accurate prediction of the image quality. Results are compared with recent works and the proposed algorithm obtains the best performance. The same pipeline is then considered to evaluate the likability and competence induced by a facial picture, with the difference that the estimation is based on high-level attributes such as gender, age and smile. Performance of these attributes is compared with previous techniques that mostly rely on facial keypoints positions, and it is shown that it is possible to obtain predictions that are close to human perception. Finally, a combination of both models that selects a likable facial image of good aesthetic quality for a given person is described.Avec le développement des appareils photos numériques et des sites de partage de photos, nous passons une part croissante de notre temps à observer, sélectionner et partager des images, parmi lesquelles figurent un grand nombre de photos de visage. Dans cette thèse, nous nous proposons de créer un premier système entièrement automatique renvoyant une estimation de la pertinence d'une photo de visage pour son utilisation dans la création d'un album de photos, la sélection de photos pour un réseau social ou professionnel, etc. Pour cela, nous créons plusieurs modèles d'estimation de la pertinence d'une photo de visage en fonction de son utilisation. Dans un premier temps, nous adaptons les modèles d'estimation de la qualité esthétique d'une photo au cas particulier des photos de visage. Nous montrons que le fait de calculer 15 caractéristiques décrivant différents aspects de l'image (texture, illumination, couleurs) dans des régions spécifiques de l'image (le visage, les yeux, la bouche) améliore significativement la précision des estimations par rapport aux modèles de l'état de l'art. La précision de ce modèle est renforcée par la sélection de caractéristiques adaptées à notre problème, ainsi que par la fusion des prédictions de 4 algorithmes d'apprentissage. Dans un second temps, nous proposons d'enrichir l'évaluation automatique d'une photo de visage en définissant des modèles d'estimation associés à des critères tels que le degré de sympathie ou de compétence dégagé par une photo de visage. Ces modèles reposent sur l'utilisation d'attributs de haut niveau (présence de sourire, ouverture des yeux, expressions faciales), qui se montrent plus efficaces que les caractéristiques de bas niveau utilisées dans l'état de l'art (filtres de Gabor, position des points de repère du visage). Enfin, nous fusionnons ces modèles afin de sélectionner automatiquement des photos de bonne qualité esthétique et appropriées à une utilisation donnée : photos inspirant de la sympathie à partager en famille, photos dégageant une impression de compétence sur un réseau professionnel

How to predict the global instantaneous feeling induced by a facial picture ?

Avec le développement des appareils photos numériques et des sites de partage de photos, nous passons une part croissante de notre temps à observer, sélectionner et partager des images, parmi lesquelles figurent un grand nombre de photos de visage. Dans cette thèse, nous nous proposons de créer un premier système entièrement automatique renvoyant une estimation de la pertinence d'une photo de visage pour son utilisation dans la création d'un album de photos, la sélection de photos pour un réseau social ou professionnel, etc. Pour cela, nous créons plusieurs modèles d'estimation de la pertinence d'une photo de visage en fonction de son utilisation. Dans un premier temps, nous adaptons les modèles d'estimation de la qualité esthétique d'une photo au cas particulier des photos de visage. Nous montrons que le fait de calculer 15 caractéristiques décrivant différents aspects de l'image (texture, illumination, couleurs) dans des régions spécifiques de l'image (le visage, les yeux, la bouche) améliore significativement la précision des estimations par rapport aux modèles de l'état de l'art. La précision de ce modèle est renforcée par la sélection de caractéristiques adaptées à notre problème, ainsi que par la fusion des prédictions de 4 algorithmes d'apprentissage. Dans un second temps, nous proposons d'enrichir l'évaluation automatique d'une photo de visage en définissant des modèles d'estimation associés à des critères tels que le degré de sympathie ou de compétence dégagé par une photo de visage. Ces modèles reposent sur l'utilisation d'attributs de haut niveau (présence de sourire, ouverture des yeux, expressions faciales), qui se montrent plus efficaces que les caractéristiques de bas niveau utilisées dans l'état de l'art (filtres de Gabor, position des points de repère du visage). Enfin, nous fusionnons ces modèles afin de sélectionner automatiquement des photos de bonne qualité esthétique et appropriées à une utilisation donnée : photos inspirant de la sympathie à partager en famille, photos dégageant une impression de compétence sur un réseau professionnel.Picture selection is a time-consuming task for humans and a real challenge for machines, which have to retrieve complex and subjective information from image pixels. An automated system that infers human feelings from digital portraits would be of great help for profile picture selection, photo album creation or photo editing. In this work, several models of facial pictures evaluation are defined. The first one predicts the overall aesthetic quality of a facial image by computing 15 features that encode low-level statistics in different image regions (face, eyes and mouth). Relevant features are automatically selected by a feature ranking technique, and the outputs of 4 learning algorithms are fused in order to make a robust and accurate prediction of the image quality. Results are compared with recent works and the proposed algorithm obtains the best performance. The same pipeline is then considered to evaluate the likability and competence induced by a facial picture, with the difference that the estimation is based on high-level attributes such as gender, age and smile. Performance of these attributes is compared with previous techniques that mostly rely on facial keypoints positions, and it is shown that it is possible to obtain predictions that are close to human perception. Finally, a combination of both models that selects a likable facial image of good aesthetic quality for a given person is described

Photo Rating of Facial Pictures based on Image Segmentation

International audienceA single glance at a face is enough to infer a first impression about someone. With the increasing amount of pictures available, selecting the most suitable picture for a given use is a difficult task. This work focuses on the estimation of the image quality of facial portraits. Some image quality features are extracted such as blur, color representation, illumination and it is shown that concerning facial picture rating, it is better to estimate each feature on the different picture parts (background and foreground). The performance of the proposed image quality estimator is evaluated and compared with a subjective facial picture quality estimation experimen

Quality of MALDI-TOF Mass Spectra in Routine Diagnostics: Results from an International External Quality Assessment including 36 Laboratories from 12 countries using 47 challenging bacterial strains.

OBJECTIVE MALDI-TOF MS is a widely used method for bacterial species identification. Incomplete databases and mass spectral quality (MSQ) still represent major challenges. Important proxies for MSQ are: number of detected marker masses, reproducibility, and measurement precision. We aimed to assess MSQs across diagnostic laboratories and the potential of simple workflow adaptations to improve it. METHODS For baseline MSQ assessment, 47 diverse bacterial strains which are challenging to identify by MALDI-TOF MS, were routinely measured in 36 laboratories from 12 countries, and well defined MSQ features were used. After an intervention consisting of detailed reported feedback and instructions on how to acquire MALDI-TOF mass spectra, measurements were repeated and MSQs were compared. RESULTS At baseline, we observed heterogeneous MSQ between the devices, considering the median number of marker masses detected (range = [5, 25]), reproducibility between technical replicates (range = [55%, 86%]), and measurement error (range = [147 parts per million (ppm), 588ppm]). As a general trend, the spectral quality was improved after the intervention for devices which yielded low MSQs in the baseline assessment: for 4/5 devices with a high measurement error, the measurement precision was improved (p-values<0.001, paired Wilcoxon test); for 6/10 devices, which detected a low number of marker masses, the number of detected marker masses increased (p-values<0.001, paired Wilcoxon test). CONCLUSION We have identified simple workflow adaptations, which, to some extent, improve MSQ of poorly performing devices and should be considered by laboratories yielding a low MSQ. Improving MALDI-TOF MSQ in routine diagnostics is essential for increasing the resolution of bacterial identification by MALDI-TOF MS, which is dependent on the reproducible detection of marker masses. The heterogeneity identified in this EQA requires further study