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Enhanced face aging using dual-learning and muti-attention mechanism

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Abstract

Face aging is an active area of research in multimedia applications that involves modifying a person’s facial photo to resemble their appearance at a different age. While conditional Generative Adversarial Networks (cGANs) have made significant progress in this field, most current approaches still face challenges in generating convincing age progression while preserving the subject’s identity. These limitations arise due to three main factors: i) a scarcity of long-range sequential labelled faces of the same person in existing datasets, which are required for training; ii) a focus on texture changes such as wrinkles, which neglects structural variations that are important in aging and limit the effectiveness of these models for large age spans; and iii) the tendency to preserve personal identity by minimizing the differences between inputs and synthesized results, which can result in blurry artifacts and insufficient variations. In this paper, we propose a novel approach to address these limitations called Landmark-guided Dual-learning cGAN (LDcGAN), which includes a multi-attention mechanism. Our approach uses an external landmark attention to adjust variations in facial structure and a built-in attention to emphasize the most discriminative regions relevant to aging. The primal cGAN is conditioned with age vectors and converts input faces to target ages, while the dual cGAN inverts the process by feeding the synthesized results back to the original input age range. This enables LDcGAN to improve age consistency and minimize changes that affect personal identity and background. Our approach demonstrates appealing results in terms of image quality, personal identity, and age accuracy, as confirmed by both qualitative and quantitative experiments.

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Authors and Affiliations

  1. Computer Science, Zhejiang Sci-Tech University, 928 No.2 street, Hangzhou, 310018, Zhejiang, China

    Xin Huang

  2. Computer Science, University of Guelph, 50 Stone Rd E, Guelph, N1G 2W1, ON, Canada

    Minglun Gong

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  1. Xin Huang
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  2. Minglun Gong
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Correspondence to Minglun Gong.

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Huang, X., Gong, M. Enhanced face aging using dual-learning and muti-attention mechanism. Appl Intell 53, 23383–23397 (2023). https://doi.org/10.1007/s10489-023-04713-2

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