Bi-modal Face Recognition - How combining 2D and 3D Clues Can Increase the Precision

Amel Aissaoui (1) , Jean Martinet (2, 3)

1 LIFL - FOX MIIRE
2 Université de Lille, Sciences et Technologies
3 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

This paper introduces a bi-modal face recognition approach. The objective is to study how combining depth and intensity information can increase face recognition precision. In the proposed approach, local features based on LBP (Local Binary Pattern) and DLBP (Depth Local Binary Pattern) are extracted from intensity and depth images respectively. Our approach combines the results of classifiers trained on extracted intensity and depth cues in order to identify faces. Experiments are performed on three datasets: Texas 3D face dataset, BOSPHORUS 3D face dataset and FRGC 3D face dataset. The obtained results demonstrate the enhanced performance of the proposed method compared to mono-modal (2D or 3D) face recognition. Most processes of the proposed system are performed automatically. It leads to a potential prototype of face recognition using the latest RGB-D sensors, such as Microsoft Kinect or Intel RealSense 3D Camera.

Mots clés: Face recognition; multimodal; 2D; 3D; LBP; RGB-depth

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Amel Aissaoui, Jean Martinet. Bi-modal Face Recognition - How combining 2D and 3D Clues Can Increase the Precision. International Conference on Computer Vision Theory and Applications, Mar 2015, Berlin, Germany. pp.559 - 564, ⟨10.5220/0005359605590564⟩. ⟨hal-01532817⟩