Multi-Pose Face Recognition Using Hybrid Face Features Descriptor
This is an incremental improvement for face recognition systems dealing with pose variability.
The paper tackles multi-pose face recognition by proposing a hybrid face features descriptor (HFFD) that fuses frequency-based features from wavelet and DCT analyses of 2D images, achieving better performance than recent 2D-based methods and handling large pose variations.
This paper presents a multi-pose face recognition approach using hybrid face features descriptors (HFFD). The HFFD is a face descriptor containing of rich discriminant information that is created by fusing some frequency-based features extracted using both wavelet and DCT analysis of several different poses of 2D face images. The main aim of this method is to represent the multi-pose face images using a dominant frequency component with still having reasonable achievement compared to the recent multi-pose face recognition methods. The HFFD based face recognition tends to achieve better performance than that of the recent 2D-based face recognition method. In addition, the HFFD-based face recognition also is sufficiently to handle large face variability due to face pose variations .