Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
IEEE Trans Image Process. 2013 Jan;22(1):353-62. doi: 10.1109/TIP.2012.2215617. Epub 2012 Aug 27.
Face recognition is confronted with situations in which face images are captured in various modalities, such as the visual modality, the near infrared modality, and the sketch modality. This is known as heterogeneous face recognition. To solve this problem, we propose a new method called discriminative spectral regression (DSR). The DSR maps heterogeneous face images into a common discriminative subspace in which robust classification can be achieved. In the proposed method, the subspace learning problem is transformed into a least squares problem. Different mappings should map heterogeneous images from the same class close to each other, while images from different classes should be separated as far as possible. To realize this, we introduce two novel regularization terms, which reflect the category relationships among data, into the least squares approach. Experiments conducted on two heterogeneous face databases validate the superiority of the proposed method over the previous methods.
人脸识别面临着各种模态的人脸图像采集的情况，例如视觉模态、近红外模态和素描模态。这被称为异构人脸识别。为了解决这个问题，我们提出了一种称为判别谱回归（DSR）的新方法。DSR 将异构人脸图像映射到一个共同的判别子空间中，在这个子空间中可以实现稳健的分类。在提出的方法中，子空间学习问题被转化为最小二乘问题。不同的映射应该将来自同一类的异构图像彼此靠近地映射，而来自不同类的图像应该尽可能地分开。为了实现这一点，我们将两个新的正则化项，反映数据之间的类别关系，引入到最小二乘方法中。在两个异构人脸数据库上进行的实验验证了所提出的方法优于以前的方法。
