Hybrid feature fusion for person recognition in photo albums
Sheng Li, Likun Huang, Wei Zhang, Bing Tang · 2020
The current work on person recognition in photo albums mainly utilize pure deep convolutional features to describe a person’s image. However, we observe that the hand-crafted features are usually able to provide complementary information and are more stable for identity recognition under some challenging circumstances. In view of this, we propose a novel hybrid method for person recognition in photo albums. In the proposed method, both the hand-crafted features and deep convolutional features are extracted from every person’s image. These multi-modality features are then fused by a weighted average method and classified by a pre-trained SVM in the recognition procedure. The experimental results demonstrates the effectiveness of the proposed method.