DLFace: Deep local descriptor for cross-modality face recognition.

Highlights • We develop a deep local descriptor for cross-modality face recognition, which can learn discriminant information from image patches. • We propose an enumeration loss function to eliminate modality gap on local patch level, which is integrated into a convolutional neural network. • Extensive experiments show that DLFace outperforms existing methods, which demonstrate the effectiveness of our method. Abstract Cross-modality face recognition aims to identify faces across different modalities, such as matching sketches with photos, low resolution face images with high resolution images, and near infrared images with visual lighting images, which is challenging because of the modality gap caused by texture, resolution, and illumination variations. Existing approaches either utilized hand-crafted approaches which ignore inherent data distribution characteristic, or applied deep learning-based algorithms on holistic face images with facial local information ignored. In this paper, we propose a deep local descriptor learning framework for cross-modality face recognition, which aims to learn discriminant and compact local information directly from raw facial patches. A novel cross-modality enumeration loss is proposed to eliminate the modality gap on local patch level, which is then integrated into a convolutional neural networks for deep local descriptor extraction. The proposed deep local descriptor can be easily applied to any traditional face recognition systems, and we use Fisherface as an example in the paper. Extensive experiments on six widely used cross-modality face recognition datasets demonstrate the superiority of proposed method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]