Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval

Abstract

Abstract. Recently, promising results have been shown on face recognition researches. However, face recognition and retrieval across age is still challenging. Unlike prior methods using complex models with strong parametric assumptions to model the aging process, we use a data-driven method to address this problem. We propose a novel coding framework called Cross-Age Reference Coding (CARC). By leveraging a large-scale image dataset freely available on the Internet as a reference set, CARC is able to encode the low-level feature of a face image with an ageinvariant reference space. In the testing phase, the proposed method only requires a linear projection to encode the feature and therefore it is highly scalable. To thoroughly evaluate our work, we introduce a new large-scale dataset for face recognition and retrieval across age called Cross-Age Celebrity Dataset (CACD). The dataset contains more than 160,000 images of 2,000 celebrities with age ranging from 16 to 62. To the best of our knowledge, it is by far the largest publicly available crossage face dataset. Experimental results show that the proposed method can achieve state-of-the-art performance on both our dataset as well as the other widely used dataset for face recognition across age, MORPH dataset.

Person Recognition in Personal Photo Collections

“…In comparison our cues are trained over ImageNet and PIPA's 29 · 10 3 faces over 1.4 · 10 3 persons. To measure the effect of training on larger data we consider fine-tuning using two open face recognition datasets: CASIA-WebFace (CASIA) [40] and the "Cross-Age Reference Coding Dataset" (CACD) [6]. CASIA contains 0.5 · 10 6 images of 10.5 · 10 3 persons (mainly actors and public figures), and is (to the best of our knowledge) the largest open dataset for face recognition.…”

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

“…Recently, some deep learning methods, including [33,[40][41][42][43][44], and data driven approaches, including [45,46], have been proposed for face recognition. In [33], coupled autoencoder networks have been used to recognize and retrieve face images with temporal variations.…”

“…Similarly, [40][41][42][43][44] propose different neural networks to achieve age-invariant face recognition. Data-driven methods presented in [45,46] are based on cross-aging reference sets to perform face recognition across aging.…”

Recovering Joint and Individual Components in Facial Data

“…It is considered as a very challenging task due to the fact that the face is a highly deformable object and its appearance drastically changes under different illumination conditions, expressions, and poses. Various databases that contain faces at different ages have been collected in the last couple of years [40], [41]. Although these databases contain huge number of images, they have some limitations including limited images for each subject that cover a narrow range of ages and noisy age labels, since most of them have been collected by employing automatic procedures (crawlers).…”