We report a functional magnetic resonance imaging (fMRI) adaptation study of two well-described patients, DF and PS, who present face identity recognition impairments (prosopagnosia) following brain-damage. Comparing faces to non-face objects elicited activation in all visual areas of the cortical face processing network that were spared subsequent to brain damage. The common brain lesion in th...

Faces activate specific brain regions in fMRI, including the fusiform gyrus (FG) and the posterior superior temporal sulcus (pSTS). The fact that the FG and pSTS are frequently co-activated suggests that they may interact synergistically in a distributed face processing network. Alternatively, the functions implemented by these regions may be encapsulated from each other. It has proven difficul...

Face detection and recognition is a vibrant area of biometrics with active research and commercial efforts over the last 20 years. The task of face detection is to search faces in images, reporting their positions by a bounding box. Recent studies [19, 31] have shown that face detection has already been a state-of-the-art technology in both accuracy and speed. However, face detection is not suf...

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

Ridge regression (RR) for classification is a regularized least square method to model the linear dependency between covariate variables and labels. By applying appropriate techniques to encode the multivariate labels in face recognition as the vertices of the regular simplex which can separate points with highest degree of symmetry, RR maps the face images into a face subspace where the images...

This paper presents an approach to detect facial features of multiple faces on complex background and with variable poses, illumination conditions, expressions, ages, image sizes, etc. First, the skin parts of the input image are extracted by color segmentation. Then, the candidate face regions are estimated by grouping the skin parts. Within each candidate face region, an attempt is made to fi...

The multimodal human-computer interface (HCI) called LipMouse is presented, allowing a user to work on a computer using movements and gestures made with his/her mouth only. Algorithms for lip movement tracking and lip gesture recognition are presented in details. User face images are captured with a standard webcam. Face detection is based on a cascade of boosted classifiers using Haar-like fea...

This paper presents a new lip detection method based-on normalized RGB chromaticity diagram. The method consists of three stages: face detection, lip region localization and lip detection. The popular Viola-Jones face detection technique is employed in the face detection stage. In the lip detection stage, lip color is extracted using our novel color segmentation method that exploits the distrib...

Why do some faces appear more similar than others? Beyond structural factors, we speculate that similarity is governed by the organization of faces located in a multi-dimensional face space. To test this hypothesis, we morphed a typical face with an atypical face. If similarity judgments are guided purely by their physical properties, the morph should be perceived to be equally similar to its t...

A novel feature selection and classification technique for face recognition is presented in this paper. Genetic Algorithms (GAs) for feature selection and Artificial Neural Network (ANN) for classification are incorporated in the proposed technique. The proposed GAs-ANN technique has two purposes in this research: 1) fusion and selection of features for face recognition. 2) location of signific...