Implicit and Explicit Feature Purification for Age-Invariant Facial Representation Learning

Xie, Jiucheng; Pun, Chi‐Man; Lam, Kin‐Man

doi:10.1109/tifs.2022.3142998

Cited by 12 publications

(5 citation statements)

References 46 publications

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“…Table 2 displays the superior performance of our model. Our proposed method improves accuracy by 1.95% over IEFP [18] (i.e., 95.82% → 97.77%) and 4.07% over LCAM [19] (i.e., 93.70% → 97.77%), demonstrating its effectiveness. Based on experimental results, it can be observed that while the Low-Complexity Attention Module (LCAM) approach has a slight advantage over past modules that employed channel and spatial attention, our proposed hybrid channel-spatial mechanism offers higher flexibility.…”

Section: Experiments On Agedb-30 Datasetmentioning

confidence: 71%

“…Consequently, a recent study has proposed a method for age-invariant face recognition called Implicit and Explicit Feature Purification (IEFP) [18]. This method helps to remove age information from facial features and obtain a purer representation of the same.…”

Section: General Aifr Methodsmentioning

confidence: 99%

“…MT-MIM [24] 96.10 1.67 MTLFace [23] 96.23 1.54 IEFP [18] 95.82 1.95 PocketNet [52] 96.78 0.99 LCAM [19] 93.70 4.07 Ours 97.77 -…”

Section: Methods Accuracy (%) Improvement (%)mentioning

confidence: 99%

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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: Experiments On Agedb-30 Datasetmentioning

confidence: 71%

Section: General Aifr Methodsmentioning

confidence: 99%

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

An,

2024

Electronics

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“…Lee et al proposed an inter-prototype loss to minimize the similarity between child faces [23]. Hou et al [24] and Xie et al [25] proposed to minimize the mutual information between the identity-and age-related components of the face image from the same person to reduce the effect of age variations.…”

Section: Age-invariant Face Recognition (Aifr)mentioning

confidence: 99%

When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and a New Benchmark

Huang

Zhang

Shan

2023

IEEE Trans. Pattern Anal. Mach. Intell.

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To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation between identity-and age-related features while face age synthesis (FAS) eliminates age variation by converting the faces in different age groups to the same group. However, AIFR lacks visual results for model interpretation and FAS compromises downstream recognition due to artifacts. Therefore, we propose a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn the age-invariant identity-related representation for face recognition while achieving pleasing face synthesis for model interpretation. Specifically, we propose an attention-based feature decomposition to decompose the mixed face features into two uncorrelated components-identity-and age-related features-in a spatially constrained way. Unlike the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, which can improve the age smoothness of synthesized faces through a weight-sharing strategy. Benefiting from the proposed multi-task framework, we then leverage those high-quality synthesized faces from FAS to further boost AIFR via a novel selective fine-tuning strategy. Furthermore, to advance both AIFR and FAS, we collect and release a large cross-age face dataset with age and gender annotations, and a new benchmark specifically designed for tracing long-missing children. Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance than state-of-the-art methods for both AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, obtaining competitive performance on face recognition in the wild. The source code and datasets are available at http://hzzone.github.io/MTLFace.

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“…Data augmentation is the process of adding noise to photos in order to boost the quantities [24]. The idea of data augmentation has a long history in literature and everyday life [25][26][27][28][29][30][31]. As a result, we were able to collect a pre-processed FG-NET dataset that included 5010 total face photos and an average of 60 face images per person.…”

Section: Fg-net Dataset Pre-processingmentioning

confidence: 99%

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

Okokpujie,

Subair

et al. 2023

ISI

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The advent of smart technology in urban environments has often been hailed as the solution to a plethora of contemporary urban challenges, ranging from environmental conservation to waste management and transportation. However, the critical aspect of security, encompassing crime detection and prevention, is frequently overlooked. Moreover, there is a dearth of research exploring the potential disruption of conventional face detection and recognition systems by new smart city surveillance security cameras, particularly those which autonomously update their databases. This paper addresses this gap by proposing the enhancement of security in smart cities through the development of an adaptive Age-Invariant Face Recognition (AIFR) model. A non-intrusive AIFR model was constructed using a convolutional neural network and transfer learning techniques, and was then integrated into surveillance cameras. These cameras, designed to capture the faces of city residents at regular intervals, consequently updated their databases autonomously. Upon testing, the developed model demonstrated its potential to substantially improve security by effectively detecting and identifying the residents and visitors of smart cities, and updating their database profiles. Remarkably, the model retained its effectiveness even with significant age intra-class variation, with the capability to alert relevant authorities about potential criminals or missing individuals. This research underscores the potential of adaptive face recognition systems in bolstering security measures within smart urban environments.

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Implicit and Explicit Feature Purification for Age-Invariant Facial Representation Learning

Cited by 12 publications

References 46 publications

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

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

When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and a New Benchmark

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

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