Face sketch recognition using deep learning

Face sketch recognition refers to automatically identifying a person from a set of facial photos using a face sketch. This thesis focuses on matching facial images between front face photos and front face hand-drawn sketches, and between front face photos and front face composite sketches by software. Because different visual domains, different image forms, and different collection methods exist between the matching image pairs, face sketch recognition is more difficult than traditional facial recognition. In this thesis, three novel deep learning models are presented to increase recognition accuracy on face photo-sketch datasets. An improved Siamese network combined with features extracted from an encoder-decoder network is proposed to extract more correlated features from facial photos and the corresponding face sketches. After that, attention modules are proposed to extract features from the same location in the photos and the sketches. In the third method, in order to reduce the difference between different visual domains, the images are transferred into a graph to increase the relationship for different face attributes and facial landmarks. Meanwhile, the graph neural network is utilized to learn the weights of neighbors adaptively. The first is to fuse more image features from the Siamese network and encoder-decoder network for increased the recognition results. Moreover, the attention modules can fix the similarity positions from different domain images to extract the correlated features. The visualized feature maps exhibit the correlated features which are extracted from the photo and the corresponding face sketch. In addition, a stable deep learning model based on graph structure is introduced to capture the topology of the graph and the relationship after images have been mapped into the graph structure for reducing the gap between face photos and face sketches. The experimental results show that the recognition accuracy of our proposed deep learning models can achieve the state-of-the-art on composite face sketch datasets. Meanwhile, the recognition results on hand-drawn face sketch datasets exceed other deep learning methods

Recent Advances in Deep Learning Techniques for Face Recognition

In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 168 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp. 99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing