Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

TL;DR

This paper introduces Cross-Age LFW (CALFW), a new face recognition database focusing on the challenge of recognizing faces across different ages in unconstrained environments, highlighting the limitations of existing datasets.

Contribution

The creation of CALFW, a database with 3,000 cross-age face pairs, specifically designed to evaluate and improve cross-age face recognition methods.

Findings

Accuracy drops 10-17% on CALFW compared to LFW.

Deep learning methods face increased challenges with age variation.

CALFW provides a more realistic benchmark for cross-age face recognition.

Abstract

Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification and due to big data driven machine learning methods, the performance on the database approaches nearly 100%. However, we argue that this accuracy may be too optimistic because of some limiting factors. Besides different poses, illuminations, occlusions and expressions, cross-age face is another challenge in face recognition. Different ages of the same person result in large intra-class variations and aging process is unavoidable in real world face verification. However, LFW does not pay much attention on it. Thereby we construct a Cross-Age LFW (CALFW) which deliberately searches and selects 3,000 positive face pairs with age gaps to add aging process intra-class variance. Negative pairs with same gender and race are also selected to reduce the influence of attribute difference between positive/negative pairs and achieve face verification instead of attributes classification. We evaluate several metric learning and deep learning methods on the new database. Compared to the accuracy on LFW, the accuracy drops about 10%-17% on CALFW.