Face recognition is a process of identifying people from their face images. Face recognition technology has many applications such as ATM access, verification of credit card, video surveillance etc. In this paper, we propose a novel face recognition algorithm which exploits both local and global features for feature extraction. Local features are extracted by Gabor wavelets and for global featu...

Detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. In this paper, by using composition of some kind of independent features and one of the most common appearance based approaches, and multilayered perceptron (MLP) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the pre...

Despite of achieving good performance in controlled environment, the conventional 3D face recognition systems still encounter problems in handling the large variations in lighting conditions, facial expression and head pose The humans use the hybrid approach to recognize faces and therefore in this proposed method the human face recognition ability is incorporated by combining global and local ...

Face recognition means checking for the presence of a face from a database that contains many faces. The face images considered for recognition undergo large variations due to changes in illumination conditions, viewing direction, facial expression and aging etc. The face images have similar geometrical features and hence discriminating one face from the other in the database is a challenging t...

Facial recognition and verification is a widely used biometric technology in security system. Unfortunately, face biometrics is vulnerable to spoofing attacks using photographs or videos. In this paper, we present an anisotropic diffusion-based kernel matrix model (ADKMM) for face liveness detection to prevent face spoofing attacks. We use the anisotropic diffusion to enhance the edges and boun...

In this paper, face detection problem is considered using the concepts of compressive sensing technique. This technique includes dictionary learning procedure and sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that the trained models have the least degree of coherence to each other. The novelty of the prop...