Coupled Information-Theoretic Encoding for Face Photo-Sketch Recognition

Automatic face photo-sketch recognition has important applications to law enforcement. Recent research has focused on transforming photos and sketches into the same modality for matching or developing advanced classification algorithms to reduce the modality gap between features extracted from photos and sketches. In this paper, we propose a new inter-modality face recognition approach of reducing the modality gap at the feature extraction stage. A new face descriptor based on coupled information-theoretic encoding is used to capture discriminative local face structures and to effectively match photos and sketches. Guided by maximizing the mutual information between photos and sketches in the quantized feature spaces, the coupled encoding is achieved by the proposed coupled informationtheoretic project tree, which is extended to the randomized forest to further boost the performance. We create the largest face sketch database including sketches of 1, 194 people from the FERET databas...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Wei Zhang, Xiaogang Wang, Xiaoou Tang
• Keywords: Computer Vision, CVPR 2011, Discriminative Local Face, Largest Face Sketch, Modality Gap