Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur, AJK, Pakistan.
Research Institute of Engineering and Technology, Hanyang University (ERICA), Ansan, South Korea.
PLoS One. 2024 Oct 4;19(10):e0308566. doi: 10.1371/journal.pone.0308566. eCollection 2024.
Heterogeneity of a probe image is one of the most complex challenges faced by researchers and implementers of current surveillance systems. This is due to existence of multiple cameras working in different spectral ranges in a single surveillance setup. This paper proposes two different approaches including spatial sparse representations (SSR) and frequency sparse representation (FSR) to recognize on-the-move heterogeneous face images with database of single sample per person (SSPP). SCface database, with five visual and two Infrared (IR) cameras, is taken as a benchmark for experiments, which is further confirmed using CASIA NIR-VIS 2.0 face database with 17580 visual and IR images. Similarity, comparison is performed for different scenarios such as, variation of distances from a camera and variation in sizes of face images and various visual and infrared (IR) modalities. Least square minimization based approach for finding the solution is used to match face images as it makes the recognition process simpler. A side by side comparison of both the proposed approaches with the state-of-the-art, classical, principal component analysis (PCA), kernel fisher analysis (KFA) and coupled kernel embedding (CKE) methods, along with modern low-rank preserving projection via graph regularized reconstruction (LRPP-GRR) method, is also presented. Experimental results suggest that the proposed approaches achieve superior performance.
探针图像的异质性是当前监控系统的研究人员和实施者面临的最复杂的挑战之一。这是由于在单个监控设置中存在多个在不同光谱范围内工作的摄像机。本文提出了两种不同的方法，包括空间稀疏表示（SSR）和频率稀疏表示（FSR），以识别具有单样本库（SSPP）的移动异构人脸图像。SCface 数据库，具有五个可见光和两个红外（IR）摄像机，被用作实验的基准，进一步使用具有 17580 个可见光和 IR 图像的 CASIA NIR-VIS 2.0 人脸数据库进行了确认。对不同场景进行了相似性、比较，例如，从摄像机的距离变化、人脸图像的大小变化以及各种可见光和红外（IR）模式的变化。基于最小二乘最小化的方法用于寻找解决方案，因为它使识别过程更简单。还对两种提出的方法与最先进的、经典的主成分分析（PCA）、核 Fisher 分析（KFA）和耦合核嵌入（CKE）方法以及现代基于图正则化重建的低秩保持投影（LRPP-GRR）方法进行了并排比较。实验结果表明，所提出的方法具有优越的性能。
