Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks

TL;DR

This paper introduces FIW, the largest kinship recognition dataset with over 10,000 images of 1,000 families, and provides benchmarks demonstrating the effectiveness of deep CNNs and metric learning for kinship tasks.

Contribution

The paper presents FIW, a large-scale, annotated kinship dataset, and establishes baseline benchmarks using visual features and deep learning methods for kinship verification and family recognition.

Findings

Pre-trained CNN features outperform other visual features.

Fine-tuning CNNs with triplet loss improves kinship verification accuracy.

Family-specific classifiers enhance family recognition performance.

Abstract

We present the largest kinship recognition dataset to date, Families in the Wild (FIW). Motivated by the lack of a single, unified dataset for kinship recognition, we aim to provide a dataset that captivates the interest of the research community. With only a small team, we were able to collect, organize, and label over 10,000 family photos of 1,000 families with our annotation tool designed to mark complex hierarchical relationships and local label information in a quick and efficient manner. We include several benchmarks for two image-based tasks, kinship verification and family recognition. For this, we incorporate several visual features and metric learning methods as baselines. Also, we demonstrate that a pre-trained Convolutional Neural Network (CNN) as an off-the-shelf feature extractor outperforms the other feature types. Then, results were further boosted by fine-tuning two deep CNNs on FIW data: (1) for kinship verification, a triplet loss function was learned on top of the network of pre-trained weights; (2) for family recognition, a family-specific softmax classifier was added to the network.