Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos
Autor: | Richa Singh, Afzel Noore, Naman Kohli, Mayank Vatsa, Daksha Yadav |
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Rok vydání: | 2019 |
Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Deep learning Feature extraction Computer Science - Computer Vision and Pattern Recognition Ethnic group 02 engineering and technology computer.software_genre Computer Graphics and Computer-Aided Design Autoencoder Expression (mathematics) 0202 electrical engineering electronic engineering information engineering Kinship 020201 artificial intelligence & image processing The Internet Artificial intelligence business Representation (mathematics) computer Software Natural language processing |
Zdroj: | IEEE Transactions on Image Processing. 28:1329-1341 |
ISSN: | 1941-0042 1057-7149 |
Popis: | 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 regularization 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 Comment: Accepted for publication in Transactions in Image Processing |
Databáze: | OpenAIRE |
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