Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS

Face recognition has been a long standing problem in computer vision. General face recognition is challenging because of large appearance variability due to factors including pose, ambient lighting, expression, size of the face, age, and distance from the camera, etc. There are very accurate techniques to perform face recognition in controlled environments, especially when large numbers of samples are available for each face (individual). However, face identification under uncontrolled( unconstrained) environments or with limited training data is still an unsolved problem. There are two face recognition tasks: face identification (who is who in a probe face set, given a gallery face set) and face verification (same or not, given two faces). In this work, we study both face identification and verification in unconstrained environments. Firstly, we propose a face verification framework that combines Partial Least Squares (PLS) and the One-Shot similarity model[1]. The idea is to describe a face with a large feature set combining shape, texture and color information. PLS regression is applied to perform multi-channel feature weighting on this large feature set. Finally the PLS regression is used to compute the similarity score of an image pair by One-Shot learning (using a fixed negative set). Secondly, we study face identification with image sets, where the gallery and probe are sets of face images of an individual. We model a face set by its covariance matrix (COV) which is a natural 2nd-order statistic of a sample set.By exploring an efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive a kernel function that explicitly maps the covariance matrix from the Riemannian manifold to Euclidean space. Then, discriminative learning is performed on the COV manifold: the learning aims to maximize the between-class COV distance and minimize the within-class COV distance. Sparse representation and dictionary learning have been widely used in face recognition, especially when large numbers of samples are available for each face (individual). Sparse coding is promising since it provides a more stable and discriminative face representation. In the last part of our work, we explore sparse coding and dictionary learning for face verification application. More specifically, in one approach, we apply sparse representations to face verification in two ways via a fix reference set as dictionary. In the other approach, we propose a dictionary learning framework with explicit pairwise constraints, which unifies the discriminative dictionary learning for pair matching (face verification) and classification (face recognition) problems

Effective Face Frontalization in Unconstrained Images

"Frontalization" is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face recognition systems. This, by transforming the challenging problem of recognizing faces viewed from unconstrained viewpoints to the easier problem of recognizing faces in constrained, forward facing poses. Previous frontalization methods did this by attempting to approximate 3D facial shapes for each query image. We observe that 3D face shape estimation from unconstrained photos may be a harder problem than frontalization and can potentially introduce facial misalignments. Instead, we explore the simpler approach of using a single, unmodified, 3D surface as an approximation to the shape of all input faces. We show that this leads to a straightforward, efficient and easy to implement method for frontalization. More importantly, it produces aesthetic new frontal views and is surprisingly effective when used for face recognition and gender estimation