After intensive research, heterogenous face recognition is still a
challenging problem. The main difficulties are owing to the complex
relationship between heterogenous face image spaces. The heterogeneity is
always tightly coupled with other variations, which makes the relationship of
heterogenous face images highly nonlinear. Many excellent methods have been
proposed to model the nonlinear relationship, but they apt to overfit to the
training set, due to limited samples. Inspired by the unsupervised algorithms
in deep learning, this paper proposes an novel framework for heterogeneous face
recognition. We first extract Gabor features at some localized facial points,
and then use Restricted Boltzmann Machines (RBMs) to learn a shared
representation locally to remove the heterogeneity around each facial point.
Finally, the shared representations of local RBMs are connected together and
processed by PCA. Two problems (Sketch-Photo and NIR-VIS) and three databases
are selected to evaluate the proposed method. For Sketch-Photo problem, we
obtain perfect results on the CUFS database. For NIR-VIS problem, we produce
new state-of-the-art performance on the CASIA HFB and NIR-VIS 2.0 databases.

本研究提出了一种基于深度学习无监督算法的异质人脸识别框架，通过在局部人脸点处提取 Gabor 特征，并使用 RBM 学习本地共享表示来消除每个人脸点周围的异质性。最后，将局部 RBM 的共享表示连接在一起，并通过 PCA 处理，成功地解决了 Sketch-Photo 和 NIR-VIS 问题，并在相应的数据库上取得了最佳实验结果。