This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not require any training. We represent the gallery image sets as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image in the test image set. Images of the test set are then projected onto the gallery subspaces. The residuals, calculated using the Euclidean distance between the original and the projected test images, are used as the distance metric. Three different strategies are devised to decide on the final class of the test image set. We extensively evaluated the proposed technique using both low resolution and noisy images and with less gallery data to assess the suitability of our technique for the tasks of surveillance and video-based face recognition. The experiments show that the proposed technique achieves superior classification accuracy and has a faster execution time compared with existing techniques, especially under the challenging conditions of low resolution and a limited amount of gallery and test data.

本文提出了一种基于线性回归概念的新型图像集分类技术。与大多数其他方法不同，所提出的技术不需要任何培训。我们将画廊图像集表示为高维空间中的子空间。特定类别的画廊子空间用于估计测试图像集中每个图像的回归模型。然后将测试集的图像投影到画廊子空间上。残差，使用欧几里得距离计算原始图像和投影测试图像之间的距离被用作距离度量。设计了三种不同的策略来决定测试图像集的最终类别。我们使用低分辨率和噪声图像以及较少的图库数据广泛评估了所提出的技术，以评估我们的技术对监控和基于视频的人脸识别任务的适用性。实验表明，与现有技术相比，所提出的技术实现了卓越的分类精度和更快的执行时间，尤其是在低分辨率和有限数量的画廊和测试数据的挑战性条件下。