Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos

TL;DR

This paper introduces a novel deep learning autoencoder with class-specific sparsity for kinship verification in unconstrained videos, demonstrating superior accuracy on a new and existing kinship video datasets.

Contribution

The paper proposes a new Supervised Mixed Norm Autoencoder framework for kinship verification in videos, along with a new kinship video database, improving accuracy over existing methods.

Findings

Achieved 83.18% accuracy on the KIVI database.

Outperformed existing algorithms by at least 3.2%.

Consistently yielded the best results across six kinship databases.

Abstract

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