A Study on an Automatic System for Analyzing the Facial Beauty of Young Women
Notice bibliographique
Résumé
A Study on an Automatic System for Analyzing the Facial Beauty of Young Women \n \nNeha Sultan \n \nBeauty is one of the foremost ideas that define human personality. However, only recently has the concept of beauty been scientifically analyzed. This has mostly been due to extensive research done in the area of face recognition and image processing on identification and classification of human features as contributing to facial beauty. Current research aims at precisely and conclusively understanding how humans classify a given individual's face as beautiful. Due to the lack of published theoretical standards and ground truths for human facial beauty, this is often an ambiguous process. Current methods of analysis and classification of human facial beauty rely mainly on the geometric aspects of human facial beauty. The classifiers used in current research include the k-nearest neighbor algorithm, ridge regression, and basic principal component analysis. \n \nIn this research, various approaches related to the comprehension and analysis of human beauty are presented and the use of these theories is outlined. Each set of theories is translated into a feature model that is tested for classification. Selecting the best set of features that result in the most accurate model for the representation of the human face is a key challenge. This research introduces the combined use of three main groups of features for classification of female facial beauty, to be used with classification through support vector machines. The classifier utilized is Support Vector Machine (SVM) and the accuracy obtained through this classifier is 86%. Current research in the field has produced algorithms with percentages of accuracy that are in the range of 75-85%. The approach used is one of analysis of the central tenets of beauty, the successive application of image processing techniques, and finally the usage of relevant machine learning methods to build an effective system for the automatic assessment of facial beauty. The ground truths used for verifying results are derived from ratings extracted from surveys conducted. \n \n \nThe proposed methodology involves a novel algorithm for the representation of facial beauty, which combines the use of geometric, textural, and shape based features for the analysis of facial beauty. This algorithm initially develops an overall landmark model of the entire human face. A significant advantage of this methodology is the accurate model of the human face which synthesizes the geometric, textural and shape-related aspects of the face. The landmark model is then used for extracting critical characteristics which are then used in a feature vector for training using machine learning. The features extracted help to represent facial characteristics in three major areas. Geometric features help to represent the symmetrical properties and ratio-based properties of landmarks on the face. Textural features extracted help capture information related to skin texture and composition. Finally, face shape and outline features help to categorize the overall shape of a given face, which helps to represent the given female face shape and outline for further analysis of any deviations from the basic face shapes. These features are then used in a classifier to appropriately categorize each image. The database used for the source of images contains images of female subjects from a variety of backgrounds and levels of attractiveness.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».