基于生成学习的人脸图像年龄合成与分析 | |
李佩佩 | |
2021-05 | |
页数 | 150 |
学位类型 | 博士 |
中文摘要 | 年龄是具有生物学基础的自然标志。千百年来,人类从未停止对年龄进程的 1. 提出了两种基于生成学习的人脸预处理方法,即基于生成学习的人脸姿
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英文摘要 | Age is a natural marker with a biological basis. For thousands of years, human beings have never stopped exploring the aging process, since all kinds of demographic phenomena are closely related to age. As one of the emerging directions in the field of computer vision, facial age synthesis and analysis has important theoretical significance and practical needs, such as age estimation and cross-age face recognition in the security field, and digital face aging and inverse ageing techniques in film and TV works. Although some progress has been made in related research, facial age synthesis and analysis still face many challenges. On one hand, the aging process of human faces is influenced by various factors, including internal factors such as genetics and external factors from the living environment, which are highly uncertain. On the other hand, the complex changes of face posture and expression in a non-strictly controlled environment also bring difficulties to face aging research. Based on deep generative learning, this paper investigates facial age from three aspects: facial image pre-processing, facial image age synthesis and facial image age analysis.
3. We propose two methods for facial image age analysis, i.e., age estimation algorithm with adaptive label distribution learning and a unified framework for facial age analysis based on disentangled adversarial autoencoder. However, since humans with different genders, races and any other situations may influence their facial aging appearances, age label distributions are often complicated and difficult to be modeled in a parameter way. In this paper, we propose a Label Refinery Network (LRN) with two concurrent refinery processes: label distribution refinery and slack regression refinery. Label refinery network aims to learn age label distributions progressively in an iterative manner. In this way, we can adaptively obtain the specific age label distributions for different facial images without making strong assumptions of fixed distribution formulations. To further utilize the correlations among age labels, we accordingly propose a slack regression refinery to convert the age label regression into the age interval regression. In the second task, we design a novel facial age prior to guide the aging mechanism modeling. To explore the age effects on facial images, we propose a Disentangled Adversarial Autoencoder (DAAE) to disentangle the facial images into three independent factors: age, identity and extraneous information. To avoid the "wash away" of age and identity information in the face aging process, we propose a hierarchical conditional generator by passing the disentangled identity and age embeddings to the high-level and low-level layers with class-conditional BatchNorm. Finally, a disentangled adversarial learning mechanism is introduced to boost the image quality for face aging. In this way, when manipulating the age distribution, DAAE can achieve face aging with arbitrary ages. Further, given an input face image, the mean value of the learned age posterior distribution can be treated as an age estimator. These indicate that DAAE can efficiently and accurately estimate the age distribution in a disentangling manner. DAAE is the first attempt to achieve facial age analysis tasks, including face aging with arbitrary ages, exemplar-based face aging and age estimation, in a universal framework. |
关键词 | 人脸预处理、人脸年龄合成、人脸年龄分析,生成学习,解耦表示 |
语种 | 中文 |
七大方向——子方向分类 | 生物特征识别 |
文献类型 | 学位论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44887 |
专题 | 模式识别实验室 |
推荐引用方式 GB/T 7714 | 李佩佩. 基于生成学习的人脸图像年龄合成与分析[D]. 自动化研究所智能化大厦1610. 中科院自动化研究所,2021. |
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