Deep Learning Beyond Traditional Supervision

With the rapid development of innovative models and huge success on various applications, the field of deep learning has attracted enormous attention in computer vision, machine learning, and artificial intelligence. Countless researches have validated the superior performance and unprecedented extensiveness of deep learning models, especially with the advantages of high performance computing by GPUs and parallel computation. Nonetheless, drawbacks including strong dependency on supervision (sufficient labeled data) and monotonous usage of categorized labels are negatively interfering the advancement of deep learning. In this dissertation, we plan to expose and exploit some possibilities of deep learning without using data and labels in the traditional supervision way. Specifically, we propose a pipeline to fulfill this process in a three-step manner: ranking instead of classification and regression, transfer leaning including domain adaptation, and finally data synthesis without supervised labels. First, we propose a novel ranking-based Convolutional Neural Network architecture. It can take advantage of both ranking algorithms and features learned with CNN models. Specifically, instead of using labels in classification or regression, it can take ordinal information into consideration. Meanwhile, features learned in CNN-based models can significantly outperform engineered features to achieve superior performance. Then, we propose a transfer learning framework which can also fulfill the functions of knowledge distillation and domain adaptation. In this step, we propose to solve the problem when inadequate or even no labels are available for a target domain by taking advantage of a source domain. Furthermore, our approach can utilize the information across platform and architecture as long as a forward pass of the source network is obtainable. Last, we propose an efficient and scalable model for cross-dataset one-shot person re-identification tasks. In this case, we address the problem to determine the relationship for a pair of query and gallery images from different camera styles. We adopt the concept from style transfer together with adversarial training to boost the performance and improve the robustness

Biometric information analyses using computer vision techniques.

Biometric information analysis is derived from the analysis of a series of physical and biological characteristics of a person. It is widely regarded as the most fundamental task in the realms of computer vision and machine learning. With the overwhelming power of computer vision techniques, biometric information analysis have received increasing attention in the past decades. Biometric information can be analyzed from many sources including iris, retina, voice, fingerprint, facial image or even the way one walks with. Facial image and gait, because of their easy availability, are two preferable sources of biometric information analysis. In this thesis, we investigated the development of most recent computer vision techniques and proposed various state-of-the-art models to solve the four principle problems in biometric information analysis including the age estimation, age progression, face retrieval and gait recognition. For age estimation, the modeling has always been a challenge. Existing works model the age estimation problem as either a classification or a regression problem. However, these two types of models are not able to reveal the intrinsic nature of human age. To this end, we proposed a novel hierarchical framework and a ordinal metric learning based method. In the hierarchical framework, a random forest based clustering method is introduced to find an optimal age grouping protocol. In the ordinal metric learning approach, the age estimation is solved by learning an subspace where the ordinal structure of the data is preserved. Both of them have achieved state-of-the-art performance. For face retrieval, specifically under a cross-age setting, we first proposed a novel task, that is given two images, finding the target image which is supposed to have the same identity with the first input and the same age with the second input. To tackle this task, we proposed a joint manifold learning method that can disentangle the identity with the age information. Accompanied with two independent similarity measurements, the retrieval can be easily performed. For aging progression, we also proposed a novel task that has never been considered. We devoted to fuse the identity of one image with the age of another image. By proposing a novel framework based on generative adversarial networks, our model is able to generate close-to-realistic images. Lastly, although gait recognition is an ideal long-distance biometric information task that makes up the shortfall of facial image, existing works are not able to handle large scale data with various view angles. We proposed a generative model to solve this term and achieved promising results. Moreover, our model is able to generate evidences for forensic usage

Age-related facial analysis with deep learning

Age, as an important soft biometric trait, can be inferred based on the appearance of human faces. However, compared to other facial attributes like race and gender, age is rather subtle due to the underlying conditions of individuals (i.e., their upbringing environment and genes). These uncertainties make age-related facial analysis (including age estimation, age-oriented face synthesis and age-invariant face recognition) still unsolved. In this thesis, we study these age-related problems and propose several deep learning-based methods, each tackle a problem from a specific aspect. We first propose a customised Convolutional Neural Network architecture called the FusionNet and also its extension to study the age estimation problem. Although faces are composed of numerous facial attributes, most deep learning-based methods still consider a face as a typical object and do not pay enough attention to facial regions that carry age-specific features for this particular task. Therefore, the proposed methods take several age-specific facial patches as part of the input to emphasise the learning of age-specific patches. Through extensive evaluation, we show that these methods outperform existing methods on age estimation benchmark datasets under various evaluation matrices. Then, we propose a Generative Adversarial Network (GAN) model for age-oriented face synthesis. Specifically, to ensure that the synthesised images are within target age groups, this method tackles the mode collapse issue in vanilla GANs with a novel Conditional Discriminator Pool (CDP), which consists of multiple discriminators, each targeting one particular age category. To ensure the identity information xiv is unaltered in the synthesised images, our method uses a novel Adversarial Triplet loss. This loss, which is based on the Triplet loss, adds a ranking operation to further pull the positive embedding towards the anchor embedding resulting in significantly reduced intra-class variances in the feature space. Through extensive experiments, we show that our method can precisely transform input faces into the target age category while preserving the identity information on the synthesised faces. Last but not least, we propose the disentangled contrastive learning (DCL) for unsupervised age-invariant face recognition. Different from existing AIFR methods, DCL, which aims to learn disentangled identity features, can be trained on any facial datasets and further tested on age-oriented datasets. Moreover, by utilising a set of three augmented samples derived from the same input image, Disentangled Contrastive Learning can be directly trained on small-sized datasets with promising performance. We further modify the conventional contrastive loss function to fit this training strategy with three augmented samples. We show that our method dramatically outperforms previous unsupervised methods and other contrastive learning methods

Using age information as a soft biometric trait for face image analysis

Soft biometrics refers to a group of traits that can provide some information about an individual but are inadequate for identification or recognition purposes. Age, as an important soft biometric trait, can be inferred based on the appearance of human faces. However, compared to other facial attributes like race and gender, age is rather subtle due to the underlying conditions of individuals (i.e., their upbringing environment and genes). These uncertainties make age-related face image analysis (including age estimation, age synthesis and age-invariant face recognition) still unsolved. Specifically, age estimation is concerned with inferring the specific age from human face images. Age synthesis is concerned with the rendering of face images with natural ageing or rejuvenating effects. Age-invariant face recognition involves the recognition of the identity of subjects correctly regardless of their age. Recently, thanks to the rapid development of machine learning, especially deep learning, age-related face image analysis has gained much more attention from the research community than ever before. Deep learning based models that deal with age-related face image analysis have also significantly boosted performance compared to models that only use traditional machine learning methods, such as decision trees or boost algorithms. In this chapter, we first introduce the concepts and theory behind the three main areas of age-related face image analysis and how they can be used in practical biometric applications. Then, we analyse the difficulties involved in these applications and summarise the recent progress by reviewing the state-of-the-art methods involving deep learning. Finally, we discuss the future research trends and the issues that are not addressed by existing works. We also discuss the relationship among these three areas and show how solutions within one area can help to tackle issues in the others