论文标题
增强婴儿和幼儿面部老化模拟中的性别和身份保存
Enhance Gender and Identity Preservation in Face Aging Simulation for Infants and Toddlers
论文作者
论文摘要
现实的年龄制作照片在广泛的应用中提供了宝贵的生物识别信息。近年来，基于深度学习的方法在建模人脸的衰老过程方面取得了显着进步。然而，从婴儿或幼儿照片中产生准确的年龄摄影面孔仍然是一项艰巨的任务。特别是，缺乏视觉上可检测到的性别特征和早期生活的急剧外观会导致任务困难。我们提出了一种新的深度学习方法，灵感来自成功的条件对抗自动编码器（CAAE，2017）模型。在我们的方法中，我们将CAAE体系结构扩展到1）合并性别信息，以及2）将模型的整体体系结构与基于面部特征的身份保护组件扩展。我们使用公开可用的UTKFACE数据集对模型进行了培训，并通过模拟1,156名男性和1,207位女性婴儿和幼儿的面部照片来评估我们的模型。与CAAE方法相比，我们的新模型显示出明显的视觉改进。定量地，我们的模型的总体增长为77.0％（男性）和13.8％（女性）的性别保真度，该性别分类器在整个年龄范围内为模拟照片测量了总体增益。我们的模型还表明，通过面部识别神经网络衡量的身份保存增长了22.4％。
Realistic age-progressed photos provide invaluable biometric information in a wide range of applications. In recent years, deep learning-based approaches have made remarkable progress in modeling the aging process of the human face. Nevertheless, it remains a challenging task to generate accurate age-progressed faces from infant or toddler photos. In particular, the lack of visually detectable gender characteristics and the drastic appearance changes in early life contribute to the difficulty of the task. We propose a new deep learning method inspired by the successful Conditional Adversarial Autoencoder (CAAE, 2017) model. In our approach, we extend the CAAE architecture to 1) incorporate gender information, and 2) augment the model's overall architecture with an identity-preserving component based on facial features. We trained our model using the publicly available UTKFace dataset and evaluated our model by simulating up to 100 years of aging on 1,156 male and 1,207 female infant and toddler face photos. Compared to the CAAE approach, our new model demonstrates noticeable visual improvements. Quantitatively, our model exhibits an overall gain of 77.0% (male) and 13.8% (female) in gender fidelity measured by a gender classifier for the simulated photos across the age spectrum. Our model also demonstrates a 22.4% gain in identity preservation measured by a facial recognition neural network.
