Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos

Identifying kinship relations has garnered interest due to several applications such as organizing and tagging the enormous amount of videos being uploaded on the Internet. Existing research in kinship verification primarily focuses on kinship prediction with image pairs. In this research, we propos...

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Veröffentlicht in:IEEE transactions on image processing Jg. 28; H. 3; S. 1329 - 1341
Hauptverfasser: Kohli, Naman, Yadav, Daksha, Vatsa, Mayank, Singh, Richa, Noore, Afzel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Feature extraction
Frames (data processing)
Image reconstruction
Lighting
Machine learning
Measurement
Occlusion
Videos
Weight
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Identifying kinship relations has garnered interest due to several applications such as organizing and tagging the enormous amount of videos being uploaded on the Internet. Existing research in kinship verification primarily focuses on kinship prediction with image pairs. In this research, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm AutoEncoder (SMNAE). This new autoencoder formulation introduces class-specific sparsity in the weight matrix. The proposed three-stage SMNAE based kinship verification framework utilizes the learned spatio-temporal representation in the video frames for verifying kinship in a pair of videos. A new kinship video (KIVI) database of more than 500 individuals with variations due to illumination, pose, occlusion, ethnicity, and expression is collected for this research. It comprises a total of 355 true kin video pairs with over 250 000 still frames. The effectiveness of the proposed framework is demonstrated on the KIVI database and six existing kinship databases. On the KIVI database, SMNAE yields video-based kinship verification accuracy of 83.18% which is at least 3.2% better than existing algorithms. The algorithm is also evaluated on six publicly available kinship databases and compared with best reported results. It is observed that the proposed SMNAE consistently yields best results on all the databases.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2018.2840880