Face detection is of fundamental importance in face recognition, facial expression recognition and other face biometrics related applications. The core problem of face detection is to select a subset of features from massive local appearance descriptors such as Haar features and LBP. This paper proposes a two stage feature selection method for face detection. Firstly, feature representation of ...

This paper presents a new face recognition method using virtual view-based eigenspace. This method provides a possible way to recognize human face of different views even when samples of a view are not available. To achieve this, we have developed a virtual human face generation technique that synthesizes human face of arbitrary views. By using a frontal and profile images of a specific subject...

Numerous psychophysical experiments found that humans preferably rely on a narrow band of spatial frequencies for recognition of face identity. A recently conducted theoretical study by the author suggests that this frequency preference reflects an adaptation of the brain's face processing machinery to this specific stimulus class (i.e., faces). The purpose of the present study is to examine th...

The feature dimension and redundancy can reduce the face recognition speed and rate, the shading and light changing can heavily affect the face recognition effect. So the first key to face recognition is how to effectively extract face features because the face image contains a lot of redundant information. Multi-wavelet transform has symmetry, orthogonality, compact support and high vanishing ...

We present an independent Gabor features (IGFs) method and its application to face recognition. The novelty of the IGF method comes from 1) the derivation of independent Gabor features in the feature extraction stage and 2) the development of an IGF features-based probabilistic reasoning model (PRM) classification method in the pattern recognition stage. In particular, the IGF method first deri...

PURPOSE We evaluated how the performance of recognizing familiar face images depends on the internal (eyebrows, eyes, nose, mouth) and external face features (chin, outline of face, hairline) in individuals with central vision loss. METHODS In experiment 1, we measured eye movements for four observers with central vision loss to determine whether they fixated more often on the internal or the...

Dr. Ali Mohammed Sahan IT Dept., Technical College of Management, Middle Technical University, Baghdad, Iraq AbstractFace recognition process in human perception utilizes two kinds of features. The first are global features used to describe the whole face image, while the second are local features used to describe finer details of the face. The combined face recognition methods stimulate the te...

Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning fr...

This paper describes the detection of faces in complex backgrounds where their sizes, positions and directions are arbitrary. We detect the faces by extracting face components such as eyes, a mouth and so on. We first extract face features and then calculate their likelihoods as each face component. Second we detect the face features which satisfy geometrical relations of the face. In order to ...