Achieving Better Kinship Recognition Through Better Baseline

Abstract

Recognizing blood relations using face images can be seen as an application of face recognition systems with additional restrictions. These restrictions proved to be difficult to deal with, however, recent advancements in face verification show that there is still much to gain using more data and novel ideas. As a result face recognition is a great source domain from which we can transfer the knowledge to get better performance in kinship recognition as a source domain. We present a new baseline for an automatic kinship recognition task and relatives search based on RetinaFace[1] for face registration and ArcFace[2] face verification model. With the approach described above as the foundation, we constructed a pipeline that achieved state-of-the-art performance on two tracks in the recent Recognizing Families In the Wild Data Challenge.

Accepted for the 4th Recognizing Families In the Wild Workshop

References in corpus (5)

• FaceNet: A Unified Embedding for Face Recognition and Clustering
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• RetinaFace: Single-stage Dense Face Localisation in the Wild
• Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks

Cited by in corpus (2)

• Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making
• Automatic Face Understanding: Recognizing Families in Photos