Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Spectral-spatial Feature Extraction for Hyperspectral Image Classification

As an emerging technology, hyperspectral imaging provides huge opportunities in both remote sensing and computer vision. The advantage of hyperspectral imaging comes from the high resolution and wide range in the electromagnetic spectral domain which reflects the intrinsic properties of object materials. By combining spatial and spectral information, it is possible to extract more comprehensive and discriminative representation for objects of interest than traditional methods, thus facilitating the basic pattern recognition tasks, such as object detection, recognition, and classification. With advanced imaging technologies gradually available for universities and industry, there is an increased demand to develop new methods which can fully explore the information embedded in hyperspectral images. In this thesis, three spectral-spatial feature extraction methods are developed for salient object detection, hyperspectral face recognition, and remote sensing image classification. Object detection is an important task for many applications based on hyperspectral imaging. While most traditional methods rely on the pixel-wise spectral response, many recent efforts have been put on extracting spectral-spatial features. In the first approach, we extend Itti's visual saliency model to the spectral domain and introduce the spectral-spatial distribution based saliency model for object detection. This procedure enables the extraction of salient spectral features in the scale space, which is related to the material property and spatial layout of objects. Traditional 2D face recognition has been studied for many years and achieved great success. Nonetheless, there is high demand to explore unrevealed information other than structures and textures in spatial domain in faces. Hyperspectral imaging meets such requirements by providing additional spectral information on objects, in completion to the traditional spatial features extracted in 2D images. In the second approach, we propose a novel 3D high-order texture pattern descriptor for hyperspectral face recognition, which effectively exploits both spatial and spectral features in hyperspectral images. Based on the local derivative pattern, our method encodes hyperspectral faces with multi-directional derivatives and binarization function in spectral-spatial space. Compared to traditional face recognition methods, our method can describe distinctive micro-patterns which integrate the spatial and spectral information of faces. Mathematical morphology operations are limited to extracting spatial feature in two-dimensional data and cannot cope with hyperspectral images due to so-called ordering problem. In the third approach, we propose a novel multi-dimensional morphology descriptor, tensor morphology profile~(TMP), for hyperspectral image classification. TMP is a general framework to extract multi-dimensional structures in high-dimensional data. The n-order morphology profile is proposed to work with the n-order tensor, which can capture the inner high order structures. By treating a hyperspectral image as a tensor, it is possible to extend the morphology to high dimensional data so that powerful morphological tools can be used to analyze hyperspectral images with fused spectral-spatial information. At last, we discuss the sampling strategy for the evaluation of spectral-spatial methods in remote sensing hyperspectral image classification. We find that traditional pixel-based random sampling strategy for spectral processing will lead to unfair or biased performance evaluation in the spectral-spatial processing context. When training and testing samples are randomly drawn from the same image, the dependence caused by overlap between them may be artificially enhanced by some spatial processing methods. It is hard to determine whether the improvement of classification accuracy is caused by incorporating spatial information into the classifier or by increasing the overlap between training and testing samples. To partially solve this problem, we propose a novel controlled random sampling strategy for spectral-spatial methods. It can significantly reduce the overlap between training and testing samples and provides more objective and accurate evaluation

Non-Parametric Probabilistic Image Segmentation

We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a Mixture of Gaussians) the proposed model is principled, provides both hard and probabilistic cluster assignments, as well as the ability to naturally incorporate prior knowledge. While previous probabilistic approaches are restricted to parametric models of clusters (e.g., Gaussians) we eliminate this limitation. The suggested approach does not make heavy assumptions on the shape of the clusters and can thus handle complex structures. Our experiments show that the suggested approach outperforms previous work on a variety of image segmentation tasks

Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks

3D Morphable Model (3DMM) based methods have achieved great success in recovering 3D face shapes from single-view images. However, the facial textures recovered by such methods lack the fidelity as exhibited in the input images. Recent work demonstrates high-quality facial texture recovering with generative networks trained from a large-scale database of high-resolution UV maps of face textures, which is hard to prepare and not publicly available. In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database. The main idea is to refine the initial texture generated by a 3DMM based method with facial details from the input image. To this end, we propose to use graph convolutional networks to reconstruct the detailed colors for the mesh vertices instead of reconstructing the UV map. Experiments show that our method can generate high-quality results and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.Comment: Accepted to CVPR 2020. The source code is available at https://github.com/FuxiCV/3D-Face-GCN