Xuan Zou

Bio: Xuan Zou is an academic researcher from University of Surrey. The author has contributed to research in topics: Facial recognition system & Face (geometry). The author has an hindex of 6, co-authored 10 publications receiving 366 citations.

Illumination Invariant Face Recognition: A Survey

Abstract: The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. In this paper an extensive and up-to-date survey of the existing techniques to address this problem is presented. This survey covers the passive techniques that attempt to solve the illumination problem by studying the visible light images in which face appearance has been altered by varying illumination, as well as the active techniques that aim to obtain images of face modalities invariant to environmental illumination.

Face Recognition Using Active Near-IR Illumination.

Abstract: A new approach to overcome the problem caused by illumination variation in face recognition is proposed in this paper. Active Near-Infrared (Near-IR) illumination projected by a Light Emitting Diode (LED) light source is used to provide a constant illumination. The difference between two face images captured when the LED light is on and off respectively, is the image of a face under just the LED illumination, and is independent of ambient illumination. In preliminary experiments with various ambient illuminations, significantly better results are achieved for both automatic and semi-automatic face recognition experiments on LED illuminated faces than on face images under ambient illuminations.

Ambient illumination variation removal by active Near-IR imaging

Abstract: We investigate an active illumination method to overcome the effect of illumination variation in face recognition. Active Near-Infrared (Near-IR) illumination projected by a Light Emitting Diode (LED) light source is used to provide a constant illumination. The difference between two face images captured when the LED light is on and off respectively, is the image of a face under just the LED illumination, and is independent of ambient illumination. In preliminary experiments across different illuminations, across time, and their combinations, significantly better results are achieved in both automatic and semi-automatic face recognition experiments on LED illuminated faces than on face images under ambient illuminations.

Motion compensation for face recognition based on active differential imaging

Abstract: Active differential imaging has been proved to be an effective approach to remove ambient illumination for face recognition. In this paper we address the problem caused by motion for a face recognition system based on active differential imaging. A moving face will appear at two different locations in the ambient illumination frame and combined illumination frame and as result artifacts are introduced to the difference face image. An approach based on motion compensation is proposed to deal with this problem. Experiments on moving faces demonstrate that the proposed approach leads to significant improvements in face identification and verification results.

Robust albedo estimation from face image under unknown illumination

Abstract: Uncontrolled illumination poses severe problems for face recognition in practical application scenarios. Many techniques to deal with this problem rely on illumination modeling and face relighting. In this paper we propose a robust approach to face albedo estimation in the framework of illumination modeling with Spherical Harmonics. This technique requires only a single face image under arbitrary illumination and assumes the face shape is known. The recovered face albedo facilitates face rendering under new illumination conditions which is useful for both illumination invariant face recognition and computer animation. In the proposed approach, the consequences of the violation of the assumption of validity of the spherical harmonics model are mitigated by minimising a cost function involving robust forms of the error in both the spherical harmonics model and the smoothness constraint. The robust estimation provides significantly better results than the traditional Least Squares Estimation in the experiments on a 3D face database.

Illumination Invariant Face Recognition Using Near-Infrared Images

Abstract: Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups

Facial expression recognition from near-infrared videos

Abstract: Facial expression recognition is to determine the emotional state of the face regardless of its identity Most of the existing datasets for facial expressions are captured in a visible light spectrum However, the visible light (VIS) can change with time and location, causing significant variations in appearance and texture In this paper, we present a novel research on a dynamic facial expression recognition, using near-infrared (NIR) video sequences and LBP-TOP (Local binary patterns from three orthogonal planes) feature descriptors NIR imaging combined with LBP-TOP features provide an illumination invariant description of face video sequences Appearance and motion features in slices are used for expression classification, and for this, discriminative weights are learned from training examples Furthermore, component-based facial features are presented to combine geometric and appearance information, providing an effective way for representing the facial expressions Experimental results of facial expression recognition using a novel Oulu-CASIA NIR&VIS facial expression database, a support vector machine and sparse representation classifiers show good and robust results against illumination variations This provides a baseline for future research on NIR-based facial expression recognition

Thermal cameras and applications: a survey

Abstract: Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras. This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

Deep face recognition: A survey

Abstract: Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. This emerging technique has reshaped the research landscape of face recognition (FR) since 2014, launched by the breakthroughs of DeepFace and DeepID. Since then, deep learning technique, characterized by the hierarchical architecture to stitch together pixels into invariant face representation, has dramatically improved the state-of-the-art performance and fostered successful real-world applications. In this survey, we provide a comprehensive review of the recent developments on deep FR, covering broad topics on algorithm designs, databases, protocols, and application scenes. First, we summarize different network architectures and loss functions proposed in the rapid evolution of the deep FR methods. Second, the related face processing methods are categorized into two classes: “one-to-many augmentation” and “many-to-one normalization”. Then, we summarize and compare the commonly used databases for both model training and evaluation. Third, we review miscellaneous scenes in deep FR, such as cross-factor, heterogenous, multiple-media and industrial scenes. Finally, the technical challenges and several promising directions are highlighted.

A survey on deep learning based face recognition

Abstract: Deep learning, in particular the deep convolutional neural networks, has received increasing interests in face recognition recently, and a number of deep learning methods have been proposed. This paper summarizes about 330 contributions in this area. It reviews major deep learning concepts pertinent to face image analysis and face recognition, and provides a concise overview of studies on specific face recognition problems, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching. A summary of databases used for deep face recognition is given as well. Finally, some open challenges and directions are discussed for future research.