Near-infrared Face Recognition Research Articles

SWU-NIRPV: a near-infrared pose variation face database and pose-invariant face recognition

Near-infrared (NIR) face recognition (FR) has demonstrated robustness against changes in ambient illumination, which makes it suitable for surveillance even under weak illumination conditions. However, the existing database for NIR FR only contains frontal face images, and the impact of pose variation on the robustness of NIR FR remains unascertained. We developed an NIR face database with 57 pose variations in a dark environment, which can be used in pose-invariant FR research. Convolutional neural networks (CNNs) were designed and tested in comparison to the traditional method in the database. The experimental results showed that a difference of even 10 deg between the gallery and testing sets can dramatically reduce the recognition performance. Additionally, an average accuracy of 90.58% was obtained for pose-invariant recognition by employing more pose variations in the gallery set using the CNN-based method.

Linear Cross-Modal Hash Encoding (LCMHE) for Visual and Near-Infrared Face Recognition

Face recognition in practical situations often encounters the change of acquisition sensors, i.e., the gallery images are captured in near-infrared and probe images captured in the visual domain or vice versa. Thus, for robust face recognition under such circumstance, a modality invariant representation is required. This letter proposes a hash-encoding-based descriptor to address this modality gap for face recognition problem, namely linear cross-modal hash encoding (LCMHE). LCMHE encodes the pixels of the face images based on a pretrained hash dictionary. The encoding highlights the reflectance part of the face and enables accurate recognition. We further explored the possibility of LCMHE by combining the deep-learning model to get better recognition. We experimented with publicly available CASIA NIR-VIS 2.0 databases and verified the effectiveness of our proposed letter.

A DCNN-Based Fast NIR Face Recognition System Robust to Reflected Light From Eyeglasses

Due to an increasing need for face recognition under poor lighting conditions, near infrared (NIR) face recognition based on deep convolutional neural networks (DCNN) has become an active area of research. However, in NIR face images of eyeglasses wearers, reflected light is generated around the eyes due to active NIR light sources, and it is one of the main contributors to performance degradation in NIR face recognition. In addition, there have to date been no attempts to lighten DCNN models for NIR face recognition. To solve these problems, we propose a DCNN-based fast NIR face recognition system which is robust to reflected light. This work has two main contributions: 1) We generated synthetic face images of individuals with and without eyeglasses using our proposed CycleGAN-based Glasses2Non-glasses (G2NG) data augmentation. We then constructed an augmented training database by adding the synthetic images, and the database helps to make the NIR face recognition system robust against reflected light. 2) A lightweight NIR FaceNet (LiNFNet) architecture was developed to reduce the computational complexity of the proposed system by adapting the depthwise separable convolutions and linear bottlenecks to VGGNet 16. The proposed architecture reduces the computation required, while improving the performance of NIR face recognition. Through the experiments reported in this paper, we verified that the proposed G2NG data augmentation improved the face recognition validation rate by 99.09% for NIR face images which have the reflected light from eyeglasses. Also, LiNFNet reduces the number of multiplication operations by 4.4×10 9 compared with VGGNet 16.

NIR Reflection Augmentation for DeepLearning-Based NIR Face Recognition

Face recognition using a near-infrared (NIR) sensor is widely applied to practical applications such as mobile unlocking or access control. However, unlike RGB sensors, few deep learning approaches have studied NIR face recognition. We conducted comparative experiments for the application of deep learning to NIR face recognition. To accomplish this, we gathered five public databases and trained two deep learning architectures. In our experiments, we found that simple architecture could have a competitive performance on the NIR face databases that are mostly composed of frontal face images. Furthermore, we propose a data augmentation method to train the architectures to improve recognition of users who wear glasses. With this augmented training set, the recognition rate for users who wear glasses increased by up to 16%. This result implies that the recognition of those who wear glasses can be overcome using this simple method without constructing an additional training set. Furthermore, the model that uses augmented data has symmetry with those trained with real glasses-wearing data regarding the recognition of people who wear glasses.

NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

Visible-light and near-infrared face recognition at a distance

A method to solve the problem of face recognition at a distance (FRAD) under the visible-light (VIS) and the near-infrared (NIR) spectra is presented in this work. For images taken under visible light at day time, we perform the coarse-scale alignment/enhancement to eliminate a set of unlikely candidates at the first stage. Then, the fine-scale alignment/enhancement steps are conducted to refine the candidate list furthermore iteratively at the second stage. To address the additional challenge associated with NIR images captured at night time, we incorporate a restoration mechanism that reconstructs low-quality patches through a locally linear embedding (LLE) process with a local constraint. It is shown by experimental results that our FRAD solution outperforms state-of-the-art methods on both VIS and NIR images.

Near-infrared Face Recognition by Fusion of E-GV-LBP and FKNN

To solve the problem of face recognition with complex changes and further improve the efficiency, a new near-infrared face recognition algorithm which fuses E-GV-LBP and FKNN algorithm is proposed. Firstly, it transforms near infrared face image by Gabor wavelet. Then, it extracts LBP coding feature that contains space, scale and direction information. Finally, this paper introduces an improved FKNN algorithm which is based on spatial domain. The proposed approach has brought face recognition more quickly and accurately. The experiment results show that the new algorithm has improved the recognition accuracy and computing time under the near-infrared light and other complex changes. In addition, this method can be used for face recognition under visible light as well.

A Near-Infrared Face Detection and Recognition System Using ASM and PCA+LDA

Near-infrared (NIR) images have a lot of advantages, which can make up the shortages of visible light (VL) images. A novel NIR detection and recognition system is presented in this paper. First, a NIR imaging system is developed to provide good illumination conditions for subsequent face detection and recognition. Then, Active Shape Model (ASM) method is applied to detect the features and the low-dimensional Gabor features are extracted for recognition by combining Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The recognition rate of the system is up to 90% in a few milliseconds. Finally, by using such real-time NIR face recognition system, comparative results are provided, from that we can seen that this system can work robustly and effectively

Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis

Face recognition is a rapidly growing research area, which is based heavily on the methods of machine learning, computer vision, and image processing. We propose a rotation and noise invariant near-infrared face-recognition system using an orthogonal invariant moment, namely, Zernike moments (ZMs) as a feature extractor in the near-infrared domain and spectral regression discriminant analysis (SRDA) as an efficient algorithm to decrease the computational complexity of the system, enhance the discrimination power of features, and solve the “small sample size” problem simultaneously. Experimental results based on the CASIA NIR database show the noise robustness and rotation invariance of the proposed approach. Further analysis shows that SRDA as a sophisticated technique, improves the accuracy and time complexity of the system compared with other data reduction methods such as linear discriminant analysis.

Directional binary code with application to PolyU near-infrared face database

This paper introduces the establishment of PolyU near-infrared face database (PolyU-NIRFD) and presents a new coding scheme for face recognition. The PolyU-NIRFD contains images from 350 subjects, each contributing about 100 samples with variations of pose, expression, focus, scale, time, etc. In total, 35,000 samples were collected in the database. The PolyU-NIRFD provides a platform for researchers to develop and evaluate various near-infrared face recognition techniques under large scale, controlled and uncontrolled conditions. A new coding scheme, namely directional binary code (DBC), is then proposed for near-infrared face recognition. Finally, we provide three protocols to evaluate and compare the proposed DBC method with baseline face recognition methods, including Gabor based Eigenface, Fisherface and LBP (local binary pattern) on the PolyU-NIRFD database. In addition, we also conduct experiments on the visible light band FERET database to further validate the proposed DBC scheme.