# Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
###### Abstract  
Deep-learning-based, cross-age face recognition has improved significantly in recent years. However, when using the discriminative method, it is still challenging to extract robust age-invariant features that can reduce the interference caused by age. In this paper, we propose a novel, effective, attention-based feature decomposition model, the age-invariant features extraction network, which can learn more discriminative feature representations and reduce the disturbance caused by aging. Our method uses an efficient channel attention block-based feature decomposition module to extract age-independent identity features from facial representations. Our end-to-end framework learns the age-invariant features directly, which is more convenient and can greatly reduce training complexity compared with existing multi-stage training methods. In addition, we propose a direct sum loss function to reduce the interference of age-related features. Our method achieves a comparable and stable performance. Experimental results demonstrate superior performance on four benchmarked datasets over the state-of-the-art. We obtain the relative improvements of 0.06%, 0.2%, and 2.2% on the cross-age datasets CACD-VS, AgeDB, and CALFW, respectively, and a relative 0.03% improvement on a general dataset LFW. convolutional neural networks; cross-age face recognition; deep learning; attention mechanism +
Footnote †: journal:  
Existing methods for CAFR can be divided into two categories: generative approaches and discriminative approaches. The generative approaches [14; 15; 16; 17; 18; 19] synthesize a desired image into the target age group to assist with face recognition. The downside of the generative models is their high computational costs caused by the high complexity involved in modeling aged faces. In addition, various objective functions are required to limit the generator to just creating high-quality faces. The discriminative approaches [20; 21] address CAFR tasks by extracting age-invariant representations. The development of deep convolutional neural networks (CNNs) has enabled a breakthrough in discriminative approaches in recent years. The deep features extracted from the face images at different ages usually contain two types of information, which is related to the age and face identity, respectively. Therefore, the cross-age discriminative models focus on how to separate the identity-dependent components from the extracted facial features [19; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. The main limitation of multi-stage training methods is the difficulty in accelerating model convergence. For example, [PERSON] et al. [30] proposed the decorrelated adversarial learning (DAL) algorithm to achieve feature decomposition in an adversarial manner. The alternative training feature extraction and decomposition modules make it difficult to converge. Therefore, we propose an end-to-end training model, including an efficient feature decomposition module and a novel loss function to sufficiently separate age information and identity information from facial features. Another limitation is that existing methods to train a high-performance model usually require a large-scale cross-age face dataset with good age labels and a wide age gap, which most current public databases lack. Therefore, we adopt an FR network, which is pre-trained on the general face dataset MS-Celeb-1M, as a backbone to extract facial features. Recently, visual attention mechanisms and residual learning have been widely used to solve vision problems such as image classification [32; 33]. Attention mechanism-based approaches use channel attention to capture essential features. Therefore, we can extract age-related features in facial representations using channel attention. In this paper, we propose a novel, effective, attention-based feature decomposition model for CAFR that can learn many discriminative feature representations and reduce the disturbance caused by aging.