As it becoming more and more reliable and mature, the technology of face recognition has been widely applied to nowadays life. However, conventionally, face recognition uses the visible light as the imaging spectrum and is thus limited under lighting conditions (such as nighttime) and bad atmospheric conditions (such as rain, fog). Infrared face imaging provides an alternative solution to above problem. Nonetheless, infrared (IR) facial images are usually in low quality due to limitation of current imaging devices as well as atmospheric noises and disturbance. This situation restrains the face recognition system from performing well. Therefore, enhancing of low-quality IR facial images is crucial to a practical IR face recognition system. We in this research work propose to address the problem of IR facial image enhancement by a succession of IR facial denoising and IR facial deblurring. The former is realized via a deep neural network of denoising while the latter is achieved by a blind deconvolution algorithm. The denoising DNN is trained on the Waterloo Exploration database and tested on our multispectral face dataset. The metrics of PSNR and running time are used to compare between different denoising methods including both traditional ones and deep learning-based ones. The metric of Tenegrad is used to evaluate the deblurring method involved. Overall, image quality is improved significantly, which in turn proves our proposed framework of successive IR face enhancement to be beneficial.
Face detection is one of the most important research topics in the field of computer vision, and it is also the premise and an essential part of face recognition. With the advent of deep learning-based techniques, the performance of face detection has been largely improved and more and more daily applications have been witnessed. However, face detection is greatly affected by environmental illumination. Most of existing face detection algorithms neglect harsh illumination conditions such as nighttime condition where lighting is insufficient or it is totally dark. These conditions are often encountered in real-world scenarios, e.g., nighttime surveillance in law enforcement or civil settings. How to overcome the problem of face detection in the darkness becomes a critical and urgent demand. We thus in this paper study face detection in the darkness using infrared (IR) imaging. We build an IR face detection dataset and design a deep learning-based model to study the face detection performance. Specifically, the deep learning model is a Single Stage Detector which has the advantage of fast speed and lower computation cost compared with other face detectors that consists of multiple stages. In the experiment, we also compare the performance of our deep learning model with that of a well-known traditional face detection algorithm, AdaBoost. In terms of True Positive Rate (TPR), our model significantly outperforms AdaBoost by 5% -- a dramatic boost from 87% to 92%, which suggests our deep learning-based method with IR imaging can indeed meet the requirement of real-world nighttime face detection applications.
The periocular region is considered as a relatively new modality of biometrics and serves as a substitute solution for face recognition with occlusion. Moreover, many application scenarios occur at nighttime, such as nighttime surveillance. To address this problem, we study the topic of periocular recognition at nighttime using the infrared spectrum. Utilizing a simplified version of DeepFace, a convolutional neural networks designed for face recognition, we investigate nighttime periocular recognition at both short and long standoffs, namely 1.5 m, 50 m and 106 m. A subband of the active infrared spectrum { near-infrared (NIR) { is involved. During generation of the periocular dataset, preprocessing is conducted on the original face images, including alignment, cropping and intensity conversion. The verification results of the periocular region using DeepFace are compared with the results of two conventional methods { LBP and PCA. Experiments have shown that the DeepFace algorithm performs fairly well (with GAR over 90% at FAR=0.1%) using the periocular region as a modality even at nighttime. The framework also shows superiority to both LBP and PCA in all cases of different light wavelengths and standoffs.
Cross-spectral matching of active infrared (IR) facial probes to a visible light facial gallery is a new challenging problem. This scenario is brought up by a number of real-world surveillance tasks such as recognition of subjects at night or under severe atmospheric conditions. When combined with long distance, this problem becomes even more challenging due to deteriorated quality of the IR data, causing another issue called image quality disparity between the visible light and the IR imagery. To address this quality disparity in the heterogeneous images due to atmospheric and camera effects - typical degrading factors observed in long range IR data, we propose an image fusion-based method which fuses multiple IR facial images together and yields a higher-quality IR facial image. Wavelet decomposition using the Harr basis is conducted first and then the coefficients are merged according to a rule that treats the high and low frequencies differently, followed by an inverse wavelet transform step to reconstruct the final higher-quality IR facial image. Two sub-bands of the IR spectrum, namely short-wave infrared (SWIR) and near-infrared (NIR), as well as two different long standoffs of 50 m and 106 m are involved. Experiments show that in all cases of different sub-bands and standoffs our image fusion-based method outperforms the one without image fusion, with GARs significantly increased by 3.51% and 1.09% for SWIR 50 m and NIR 50 m at FAR=10%, respectively. The equal error rates are reduced by 2.61% and 0.90% for SWIR 50 m and NIR 50 m, respectively.
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