Performance of many state-of-the-art face recognition (FR) methods deteriorates rapidly, when large in size databases are considered. In this paper, we propose a novel clustering method based on a linear discriminant analysis methodology which deals with the problem of FR on a large-scale database. Contrary to traditional clustering methods such as K-means, which are based on certain “similarit...

The image space, scale and orientation domains can give valuable clues not seen in either individual of the domains. First we decomposed the face image into different orientation and scale by Gabor filter. Second, we combine local binary pattern analysis with Gabor. It gives a good face representation for recognition. Then we classify in discriminant based up on median histogram distance. The f...

Many approaches to unconstrained face identification exploit small patches which are unaffected by distortions outside of their locality. However, small patches have limited discriminative ability, making accurate patch matching difficult. We propose a novel blockbased approach to exploit the greater discriminative information in larger areas, while maintaining robustness to local variations. A...

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low comp...

The level of security at a facility is directly related to how well the facility can keep track of “who is where?” The “who” part of this question is typically addressed through the use of face images for recognition either by a person or a computer face recognition system. The “where” part of this question can be addressed through 3D position tracking. The “who is where” problem is inherently ...

----------------------------------------------------------------------ABSTRACT-------------------------------------------------------------------This paper presents a novel approach in face digital image searching and matching. The given key image is converted into gray scale image and after that a matrix is computed with gray scale values of the key image. Then we are collecting the diagonal k...

In this paper, we propose a novel representation, called Multiscale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. InMB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code p...

End-stopped cells in cortical area V1, which combine outputs of complex cells tuned to different orientations, serve to detect line and edge crossings (junctions) and points with a large curvature. In this paper we study the importance of the multi-scale keypoint representation, i.e. retinotopic keypoint maps which are tuned to different spatial frequencies (scale or Level-of-Detail). We show t...

This paper presents a generic classification framework for large-scale face recognition systems. Within the framework, a data sampling strategy is proposed to tackle the data imbalance when image pairs are sampled from thousands of face images for preparing a training dataset. A modified kernel Fisher discriminant classifier is proposed to make it computationally feasible to train the kernel-ba...