Illumination invariant face recognition

Few of the face recognition methods reported in the literature are capable of recognising faces under varying illumination conditions. The paper discusses a method which can achieve a higher recognition rate than those obtained for existing methods. The novelty of this new method is the use of an embossing technique to process a face image before presenting it to a standard face recognition system. Using a large database of face images, the performance of the proposed method is evaluated by comparing it against the performances of three existing methods. The experimental results demonstrate the successfulness of the proposed method

Face set classification using maximally probable mutual modes

In this paper we consider face recognition from sets of face images and, in particular, recognition invariance to illumination. The main contribution is an algorithm based on the novel concept of maximally probable mutual modes (MMPM). Specifically: (i) we discuss and derive a local manifold illumination invariant and (ii) show how the invariant naturally leads to a formulation of "common modes" of two face appearance distributions. Recognition is then performed by finding the most probable mode, which is shown to be an eigenvalue problem. The effectiveness of the proposed method is demonstrated empirically on a challenging database containing the total of 700 video sequences of 100 individual

Robust face recognition by an albedo based 3D morphable model

Large pose and illumination variations are very challenging for face recognition. The 3D Morphable Model (3DMM) approach is one of the effective methods for pose and illumination invariant face recognition. However, it is very difficult for the 3DMM to recover the illumination of the 2D input image because the ratio of the albedo and illumination contributions in a pixel intensity is ambiguous. Unlike the traditional idea of separating the albedo and illumination contributions using a 3DMM, we propose a novel Albedo Based 3D Morphable Model (AB3DMM), which removes the illumination component from the images using illumination normalisation in a preprocessing step. A comparative study of different illumination normalisation methods for this step is conducted on PIE and Multi-PIE databases. The results show that overall performance of our method outperforms state-of-the-art methods

Online Face Recognition with Application to Proactive Augmented Reality

Recently, more and more researchers have concentrated on the research of video-based face recognition. The topic of this thesis is online face recognition with application to proactive augmented reality. We intend to solve online single-image and multiple-image face recognition problems when the influence of illumination variations is introduced. First, three machine learning approaches are utilized in single-image face recognition: PCA-based, 2DPCA-based, and SVM-based approaches. Illumination variations are big obstacles for face recognition. The next step in our approach therefore involves illumination normalization. Image preprocessing (AHE+RGIC) and invariant feature extraction (Eigenphases and LBP) methods are employed to compensate for illumination variations. Finally, in order to improve the recognition performance, we propose several novel algorithms to multiple-image face recognition which consider the multiple images as query data for subsequent classification. These algorithms are called MIK-NN, MMIK-NN and Kmeans+Muliple K-NN. In conclusion, the simulation experiment results show that the LBP+x2-based method efficiently compensates for the illumination effect and MMIK-NN considerably improves the performance of online face recognition