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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wu, J. Ishwar, P. Konrad, J. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Boston Univ., Boston, MA, USA (Wu, J.; Ishwar, P.; Konrad, J.) |
| Abstract | Since its release, the Kinect has been successfully used in gesture recognition. Recent work has extended Kinect's use towards biometric user authentication based on face, speech, gait, and gestures. Our work expands on the last of these modalities - gestures, which have yielded promising authentication results in prior work. This paper aims to gain insight into how authentication methods that are based on silhouette features compare against those that are based on skeletal features in terms of trade-offs between authentication performance and robustness against some real-world degradations. On a dataset of 40 users that contains two types of degradations namely, user-memory and personal-effects (heavy coats, bags, etc.), we found that for user-defined gestures, skeletal features outperform silhouettes on average by 4.89% in terms of the Equal Error Rate (EER). |
| Starting Page | 99 |
| Ending Page | 106 |
| File Size | 1004605 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479948710 |
| DOI | 10.1109/AVSS.2014.6918651 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-26 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Degradation Authentication Feature extraction Vectors Joints Covariance matrices |
| Content Type | Text |
| Resource Type | Article |
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