Deep Kinship Verification via Appearance-shape Joint Prediction and Adaptation-based Approach
This work addresses kinship verification, a challenging problem in computer vision with applications in areas like forensics and social media, but it appears incremental as it builds on existing face recognition networks and combines known feature types.
The paper tackles kinship verification from face images by proposing a deep learning pipeline that jointly predicts appearance and shape features, achieving superior performance over state-of-the-art methods on a widely used benchmark.
Kinship verification aims to identify the kin relation between two given face images. It is a very challenging problem due to the lack of training data and facial similarity variations between kinship pairs. In this work, we build a novel appearance and shape based deep learning pipeline. First we adopt the knowledge learned from general face recognition network to learn general facial features. Afterwards, we learn kinship oriented appearance and shape features from kinship pairs and combine them for the final prediction. We have evaluated the model performance on a widely used popular benchmark and demonstrated the superiority over the state-of-the-art.