Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition

Abstract

As facial appearance is subject to significant intra-class variations caused by the aging process over time, age-invariant face recognition (AIFR) remains a major challenge in face recognition community. To reduce the intra-class discrepancy caused by the aging, in this paper we propose a novel approach (namely, Orthogonal Embedding CNNs, or OE-CNNs) to learn the age-invariant deep face features. Specifically, we decompose deep face features into two orthogonal components to represent age-related and identity-related features. As a result, identity-related features that are robust to aging are then used for AIFR. Besides, for complementing the existing cross-age datasets and advancing the research in this field, we construct a brand-new large-scale Cross-Age Face dataset (CAF). Extensive experiments conducted on the three public domain face aging datasets (MORPH Album 2, CACD-VS and FG-NET) have shown the effectiveness of the proposed approach and the value of the constructed CAF dataset on AIFR. Benchmarking our algorithm on one of the most popular general face recognition (GFR) dataset LFW additionally demonstrates the comparable generalization performance on GFR.Recent AIFR researches primarily concentrate on two technical schemes: generative scheme and discriminative scheme. The generative scheme models the AIFR by synthesizing faces to one or more fixed age category then performs recognition with the artificial face representations [9,16,30].

Decorrelated Adversarial Learning for Age-Invariant Face Recognition

“…First, the DAL regularization on features is helpful to encourage the uncorrelated and co-invariant information between the decomposed components. Related works such as HFA [13], LF-CNN [48] and OE-CNN [46] have neglected the underlying correlation. Instead, we aim to minimize the classification error as well as the correlation effect simultaneously.…”

“…The [48] is based on similar analysis and extends the HFA to the deep learning framework. More recently, the OE-CNN [46] presents the orthogonal feature decomposition to solve the AIFR. According to all these studies, feature decomposition plays a key role in invariant feature learning under the assumption that facial information can be perfectly modeled by the decomposed components.…”

“…The [15] introduced an effective maximum entropy feature descriptor and a robust identity matching framework for AIFR. Several recent methods [48,59,46] are mainly based on deep neural networks. The [48] developed the Latent Factor guided Convolutional Neural Network (LF-CNN) to improve the HFA.…”

“…Network Architecture. (1) Backbone: our backbone network is a 64-layer CNN similar to [46] . It consists of 4 stages with respectively 3, 4, 10, 3 stacked residual blocks.…”

“…Thus, the discriminating power of the learned identity features can be effectively improved by the proposed DAL method. Method #Test Subjects Rank-1 HFA [13] 10,000 91.14% CARC [5] 10,000 92.80% MEFA [15] 10,000 93.80% MEFA+SIFT+MLBP [15] 10,000 94.59% LPS+HFA [24] 10,000 94.87% LF-CNNs [48] 10,000 97.51% OE-CNNs 10,000 98.55% Ours 10,000 98.93% GSM [27] 3,000 94.40% AE-CNNs [59] 3,000 98.13% OE-CNNs [46] 3,000 98.67% Ours 3,000 98.97% Quantitative Evaluation. To show the impact of the joint learning framework with our proposed DAL method, we conduct the ablative evaluations on several public AIFR datasets including FG-NET, MORPH Album 2 and CACD-VS.…”