Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning
This work addresses kinship verification for computer vision applications, showing incremental improvements by fusing features and using videos instead of still images.
The paper tackled kinship verification from facial videos by combining spatio-temporal texture features and deep learning, achieving significant performance improvements over state-of-the-art methods on the UvA-NEMO Smile database.
Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, we approach this problem from spatio-temporal point of view and explore the use of both shallow texture features and deep features for characterizing faces. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smile database. Our extensive experiments also show the superiority of using videos over still images, hence pointing out the important role of facial dynamics in kinship verification. Furthermore, the fusion of the two types of features (i.e. shallow spatio-temporal texture features and deep features) shows significant performance improvements compared to state-of-the-art methods.