Facial Kinship Verification with Large Age Variation Using Deep Linear Metric Learning
Journal of Image and Graphics
Facial appearance affects how humans interact. It is how relatives are visually identified to determine how social interactions proceed. Humans can identify kin relations based only on the face. Intrinsically, giving the ability to detect kin relations to computers can improve their usefulness in our daily lives. This research proposed a solution to the kinship verification problem with a novel non-context-aware approach using a dataset with large age variation by applying our proposed method
... ep Linear Metric Learning(DLML). Our method leverages multiple deep learning architectures trained with massive facial datasets. The knowledge acquired on traditional facial recognition tasks is re-purposed to feed a linear metric learning model. The proposed method was able to achieve better performance than other context-aware methods on tests that are inherently more difficult than the ones used on previous methods with the UB Kinface dataset. The results show that our method can use the knowledge of deep learning architectures trained to perform mainstream facial recognition tasks with massive datasets to solve kinship verification on the UB Kinface database with robustness towards large age differences present on the dataset. Our method also offers enhanced applicability when compared to previous methods on real-world situations, because it removes the necessity of knowing/detecting and treating large age variations to perform kinship verification. ii SUMARY ABSTRACT .