|
Résumé :
|
Soft biometrics topic attracted a lot of attention recently due to its ability to improve biometrics systems. It has a lot of traits which can be used in biometrics. Some of these traits is most popular among the other traits. These traits are called demographic traits (ie. age, gender, and ethnicity). It belongs to facial soft biometrics traits. Recently, several applications that exploit demographic attributes have emerged. These applications include : access control, reidentification in surveillance videos, integrity of face images in social media, intelligent advertising, human-computer interaction, and law enforcement. In this dissertation, facial demographic estimation through facial images is studied. Starting with the existing techniques like Deep Learning-based approaches, Image-Based approaches, and Anthropometrics-based approaches. Also, the databases used for age estimation, gender classification or ethnicity classification are exploited. Moreover, the different evaluation terms are mentioned. Ending with the proposed approach and the results on different databases. The proposed approach consists of the following three main stages: 1) face alignment and preprocessing; 2) feature extraction and selection; 3) demographic estimation. The purpose of face alignment is to localize faces in images, rectify the 2D or 3D pose of each face and crop the region of interest. This preprocessing stage is important since the subsequent stages depend on it and since it can affect the final performance of the system. The processing stage can be challenging since it should overcome many variations that may appear in the face image. Feature extraction and selection stage extract the face features. These features are extracted either by a holistic method or by a local method. The extracted features are then selected using a supervised feature selection method in order to omit possible irrelevant features. In the last stage, we propose to feed the obtained features to a hierarchical estimator having three layers where we firstly classify the ethnicity and the gender then we estimate the age. Finally, the obtained results using different databases was stable and good compared with the state of the art methods. The proposed approach is also suited for real-time applications.
|
