MAM: Multiple Attention Mechanism Neural Networks for Cross-Age Face Recognition

Ren, Xuhua; Wang, Jialve; Li, Shenghong

doi:10.1155/2022/8546029

Cited by 4 publications

(1 citation statement)

References 28 publications

(76 reference statements)

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“…This paper proposes a novel face recognition framework that incorporates a Hybrid Spatial-Channel Attention Module (HSCAM) to accurately extract identity features from facial images while disregarding age information [30]. The overall framework consists of three modules: the face feature extraction module, the separated age identity feature module, and the cross-age recognition module, as illustrated in Figure 2.…”

Section: Overall Frameworkmentioning

confidence: 99%

Hybrid Spatial-Channel Attention Mechanism for Cross-Age Face Recognition

An,

2024

Electronics

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Face recognition techniques have been widely employed in real-world biomimetics applications. However, traditional approaches have limitations in recognizing faces correctly with large age differences because of significant changes over age in the same person, leading to unsatisfactory recognition performance. To address this, previous studies propose to decompose and identify age and identity features independently in facial images across diverse age groups when optimizing the discriminative model so as to improve the age-invariant face recognition accuracy. Nevertheless, the interrelationships between these features make it difficult for the decomposition to disentangle them properly, thus compromising the recognition accuracy due to the interactive impacts on both features. To this end, this paper proposes a novel deep framework that incorporates a novel Hybrid Spatial-Channel Attention Module to facilitate the cross-age face recognition task. Particularly, the proposed module enables better decomposition of the facial features in both spatial and channel dimensions with attention mechanisms simultaneously while mitigating the impact of age variation on the recognition performance. Beyond this, diverse pooling strategies are also combined when applying those spatial and channel attention mechanisms, which allows the module to generate discriminative face representations while preserving complete information within the original features, further yielding sounder recognition accuracy. The proposed model is extensively validated through experiments on public face datasets such as CACD-VS, AgeDB-30, and FGNET, where the results show significant performance improvements compared to competitive baselines.

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Section: Overall Frameworkmentioning

confidence: 99%

Hybrid Spatial-Channel Attention Mechanism for Cross-Age Face Recognition

An,

2024

Electronics

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Recurrent Age Recognition Based on Manifold Learning

Zhang,

Lin,

Zhou

et al. 2024

Communications in Computer and Information Science

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Class‐wised domain decoupling‐guided adversarial feature learning for cross‐age face recognition

Wu,

Hu,

Chen

2024

Electronics Letters

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How to extract age‐independent identity features from face images has long been the major challenge in cross‐age face recognition task. In this letter, a class‐wised domain decoupling‐guided adversarial feature learning network to extract age‐independent face features is proposed. In this model, an adversarial feature learning module is designed to decompose identity features and age features. Considering that artificially designed age interference suppression strategies are difficult to fit the complex relationship between identity features and age features, an adversarial training strategy incorporated with an attention mechanism in the adversarial feature learning module is introduced to suppress age interference adaptively. Besides, under the idea of maximizing the distribution difference of identity feature domain and age feature domain, a class‐wised domain decoupling module is further designed to guide the model to extract age‐independent identity features. The proposed model has been validated on the well‐known public datasets CALFW and CACD‐VS, where it achieved remarkable recognition accuracy rates of 94.5% and 99.5%, respectively. This represents improvements of 4.2% and 0.1% over the state‐of‐the‐art single‐task‐based models, clearly showcasing the effectiveness of the proposed class‐wised domain decoupling‐guided adversarial feature learning model.

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MAM: Multiple Attention Mechanism Neural Networks for Cross-Age Face Recognition

Cited by 4 publications

References 28 publications

Hybrid Spatial-Channel Attention Mechanism for Cross-Age Face Recognition

Hybrid Spatial-Channel Attention Mechanism for Cross-Age Face Recognition

Recurrent Age Recognition Based on Manifold Learning

Class‐wised domain decoupling‐guided adversarial feature learning for cross‐age face recognition

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