Retrieval of Family Members Using Siamese Neural Network

TL;DR

This paper presents a deep Siamese neural network approach for retrieving family members in diverse conditions, improving kinship recognition accuracy and ranking in challenging datasets.

Contribution

The authors propose a novel Siamese network architecture with multiple similarity computation methods, achieving state-of-the-art results in kinship retrieval tasks.

Findings

Achieved first runner-up in RFIW2020 kinship challenge.

Demonstrated effectiveness of direct cosine similarity in inference.

Improved retrieval accuracy through combined backbone and training strategies.

Abstract

Retrieval of family members in the wild aims at finding family members of the given subject in the dataset, which is useful in finding the lost children and analyzing the kinship. However, due to the diversity in age, gender, pose and illumination of the collected data, this task is always challenging. To solve this problem, we propose our solution with deep Siamese neural network. Our solution can be divided into two parts: similarity computation and ranking. In training procedure, the Siamese network firstly takes two candidate images as input and produces two feature vectors. And then, the similarity between the two vectors is computed with several fully connected layers. While in inference procedure, we try another similarity computing method by dropping the followed several fully connected layers and directly computing the cosine similarity of the two feature vectors. After similarity computation, we use the ranking algorithm to merge the similarity scores with the same identity and output the ordered list according to their similarities. To gain further improvement, we try different combinations of backbones, training methods and similarity computing methods. Finally, we submit the best combination as our solution and our team(ustc-nelslip) obtains favorable result in the track3 of the RFIW2020 challenge with the first runner-up, which verifies the effectiveness of our method. Our code is available at: https://github.com/gniknoil/FG2020-kinship