RGB-D Face Recognition with Texture and Attribute Features
Face recognition algorithms generally utilize 2D images for feature extraction and matching. To achieve higher resilience towards covariates such as expression, illumination and pose, 3D face recognition algorithms are developed. While it is highly challenging to use specialized 3D sensors due to high cost, RGB-D images can be captured by low cost sensors such as Kinect. This research introduces a novel face recognition algorithm using RGB-D images. The proposed algorithm computes a descriptor based on the entropy of RGB-D faces along with the saliency feature obtained from a 2D face. Geometric facial attributes are also extracted from the depth image and face recognition is performed by fusing both the descriptor and attribute match scores. The experimental results indicate that the proposed algorithm achieves high face recognition accuracy on RGB-D images obtained using Kinect compared to existing 2D and 3D approaches.
The database can be downloaded from the following link: IIIT-D Kinect RGB-D Face Database
To obtain the password for the compressed file, kindly take a print out of the license agreement, fill it up and send the scanned copy to firstname.lastname@example.org. with the subject line "License agreement for IIIT-D Kinect RGBD Face Database".
As soon as we obtain the signed license agreement in scanned form, we would provide you with the access to our database.
NOTE: The license agreement has to be signed by faculty members or equivalent. It is not possible to accept license agreements signed by students and temporary researchers
This database is available only for research and educational purpose and not for any commercial use. If you use the database in any publications or reports, you must refer to the following papers:
- G. Goswami, M. Vatsa, and R. Singh, RGB-D Face Recognition with Texture and Attribute Features, IEEE Transactions on Information Forensics and Security, 2014.
- G. Goswami, S. Bharadwaj, M. Vatsa, and R. Singh, On RGB-D Face Recognition using Kinect, International Conference on Biometrics: Theory, Applications and Systems, 2013.