Adversarial Reconstruction CNN for Illumination-Robust Frontal Face Image Recovery and Recognition

Automatic Frontal Face Reconstruction Approach for Pose Invariant Face Recognition

Handling pose variations for face recognition system is a challenging task. The recognition rate is drastically decreasing with the images captured in uncontrolled environment having pose variations in yaw, pitch and roll angles. When the face image with frontal pose it is proved that the recognition system performs well. In this research an attempt is made to reconstruct frontal pose face imag...

Robust Face Recognition Technique under Varying Illumination

Face recognition is one of a complex biometrics in the field of pattern recognition due to the constraints imposed by variation in the appearance of facial images. These changes in appearance are affected by variation in illumination, expression or occlusions etc. Illumination can be considered a complex problem in both indoor and outdoor pattern matching. Literature studies have revealed that ...

Robust Face Recognition under various illumination conditions

Robust Face Image Matching under Illumination Variations

Face image matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, a novel face image matching algorithm robust against illumination variations is proposed. The proposed image matching algorithm is motivated by the characteristics of high image gradient along the face con...

Color Face Tensor Factorization and Slicing for Illumination-Robust Recognition

In this paper we present a face recognition method based on multiway analysis of color face images, which is robust to varying illumination conditions. Illumination changes cause large variations on color in face images. The main idea is to extract features with minimal color variations but with retaining image spatial information. We construct a tensor of color image ensemble, one of its coord...