|Thesis Advisor||胡卫明 ; 兴军亮|
|Place of Conferral||北京|
|Keyword||人脸年龄估计 深度学习 多任务学习 样本不均衡学习 代价敏感学习|
As an important face attribute, age has broad application prospects in the areas of human-computer interaction, intelligent business, security surveillance and entertainment. Automatic human age estimation, as an important biometric recognition technology, has become a hot research topic in the field of pattern recognition and computer vision. The definition of human age estimation is to automatically estimate the true age based on the input face image by using computer vision and other technologies. Although many researchers have made arduous efforts to solve the human age estimation problem, it is far from being solved and still faces many severe difficulties and challenges.
First of all, human growth is a continuous and slowly changing process. Therefore, the appearance differences of faces with similar ages are relatively small, which makes it difficult to manually design discriminative aging features to describe these subtle differences. Second, collecting large numbers of face images with age labels is very expensive and time-consuming. Therefore, most of the public age estimation datasets have the problem of small sample size and imbalanced age distribution, which greatly increases the difficulty of human age estimation. In addition, different populations, that is, people of different genders and races, have different aging patterns, which also brings great difficulties to age estimation.
In this thesis, focusing on the above-mentioned difficulties in the age estimation problem, we conduct a series of original research based on the deep learning technology, and propose a variety of effective deep age estimation algorithms. The main contributions of this thesis are summarized as follows:
(1) We propose a hybrid multi-task deep age estimation model. The traditional age estimation algorithms generally include two steps: the first step is to extract the manually designed aging features, and the second step is to use the extracted features to train the age estimation model. These two steps are independent of each other, so the performance of the model is heavily dependent on the quality of the extracted aging features. In recent years, deep learning has made breakthroughs in major mainstream computer vision tasks, thanks to its ability to learn features and classifiers end-to-end. In order to overcome the difficulty of manually designing aging features, we systematically analyze how to better apply deep learning to the age estimation problem. Specifically, starting from a simple baseline network architecture, we have gradually analyzed three different formulations of the age estimation problem, five different loss functions, and three different multi-task network architectures. The experimental results show that our proposed hybrid multi-task deep age estimation model has the best performance and obtains the best results on two large public age estimation datasets.
(2) We propose a deep age estimation model based on cumulative and comparative supervision signals. This model can be used to alleviate the problem of imbalanced age distribution and small sample size in age estimation datasets. First, we design a cumulative hidden layer and a cumulative supervision signal. Even if the number of samples corresponding to an age is small, by using this cumulative signal, the model can implicitly learn from faces with nearby ages, so that the problem of imbalanced age distribution can be greatly alleviated. Next, we further propose a novel comparative ranking layer and a comparative supervision signal to help the network learn more discriminative age features, thereby further improving age estimation performance. The comparative supervision signal is defined based on the information of the sample pair. Since the same sample can appear in different sample pairs, this allows the network to make full use of the training data, so it can alleviate the small sample size problem to some extent. We have validated the effectiveness of the model on two large public age estimation datasets.
(3) We propose a deep cross-population age estimation model based on cost-sensitive and order-preserving feature learning. To deal with the influence of gender and race on age estimation, it is common practice to train an age estimation model for each population separately, but it is very difficult to collect sufficient training data for each population. In practice, the most likely situation is that there are sufficient samples for some populations and fewer samples for other populations. Instead of resorting to labeling more data, it is better to exploit the existing large sized training data of one (source) population to improve the age estimation performance on another (target) population for which only a small sized set of training data is available. We design a deep cross-population age estimation model to achieve this goal. In particular, our model develops a two-stage training strategy. First, a novel cost-sensitive multi-task loss function is designed to learn transferable low-level aging features by training on the source population. Second, the high-level aging features of the source population and the target population are mapped into a unified aging feature space through the order-preserving feature alignment stage. By doing so, our model can successfully transfer the knowledge learned from the source population to the target population, and then obtain a deep age estimation model with good performances on both the source population and the target population. We validate the effectiveness of our deep cross-population age estimation model on two large public age estimation datasets.
In summary, this thesis solves some of the major difficulties in the human age estimation problem from different perspectives. The proposed algorithms greatly improve the performance of human age estimation and have obtained the best results on multiple public human age estimation datasets. At the same time, the human age estimation algorithms proposed in this thesis have been practically applied by Huawei Technologies Co., Ltd. and have achieved certain economic benefits.
|李凯. 基于深度学习的自动人脸年龄估计研究[D]. 北京. 中国科学院大学,2018.|
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