Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.

This paper addresses 3D face recognition from facial shape. Firstly, we present an effective method to automatically extract ROI of facial surface, which mainly depends on automatic detection of facial bilateral symmetry plane and localization of nose tip. Then we build a reference plane through the nose tip for calculating the relative depth values. Considering the non-rigid property of facial surface, the ROI is triangulated and parameterized into an isomorphic 2D planar circle, attempting to preserve the intrinsic geometric properties. At the same time the relative depth values are also mapped. Finally we perform eigenface on the mapped relative depth image. The entire scheme is insensitive to pose variance. The experiment using FRGC database v1.0 obtains the rank-1 identification score of 95%, which outperforms the result of the PCA base-line method by 4%, which demonstrates the effectiveness of our algorithm. 1

this paper, we investigate face recognition from range data by facial profiles and surface. An e#cient symmetry plane detection method for facial range data is presented to help extract facial profile. A global profile matching method is then exploited to align and compare the two profiles without detecting fiducial points that is often unreliable. The central profile and two kinds of horizontal profiles --- nose-crossing profile and forehead-crossing profile --- are employed in recognition. For each individual, a statistical model is built to represent the distinct discriminative capability of the di#erent regions on the facial surface. It is then incorporated into a weighted distance function to measure for the similarity of surfaces. The comparable experimental results are achieved on a facial range data database with 120 individuals

It is becoming increasingly important to be able to credential and identify authorized personnel at key points of entry. Such identity management systems commonly employ biometric identifiers. In this paper, we present a novel multimodal facial recognition approach that employs data from both visible spectrum and thermal infrared sensors. Data from multiple cameras is used to construct a threedimensional mesh representing the face and a facial thermal texture map. An annotated face model with explicit two-dimensional parameterization (UV) is then fitted to this data to construct: 1) a three-channel UV deformation image encoding geometry, and 2) a one-channel UV vasculature image encoding facial vasculature. Recognition is accomplished by comparing: 1) the parametric deformation images, 2) the parametric vasculature images, and 3) the visible spectrum texture maps. The novelty of our work lies in the use of deformation images and physiological information as means for comparison. We have performed extensive tests on the Face Recognition Grand Challenge v1.0 dataset and on our own multimodal database with very encouraging results.

In the framework of a face verification system using a Gaussian Mixture Model (GMM) based classifier, we address the problem of non-frontal face verification (when only a single (frontal) training image is available) by augmenting a client's frontal face model with artificially synthesized models for non-frontal views. Several techniques are proposed for the synthesis: "difference between two universal background models" (UBMdiff), Maximum Likelihood Linear Regression (MLLR) based, Maximum Likelihood Shift (MLS) based and standard multi-variate linear regression (LinReg) based. All techniques rely on prior information and learn how a generic face model for the frontal view is related to generic face models at non-frontal views. The synthesis and augmentation approach is evaluated by applying it to two face verification systems: PCA based and DCTmod2 based [32]; the two systems are a representation of holistic and local feature approaches, respectively. Results from experiments on the FERET database suggest that the LinReg technique (which is based on a common relation between two sets of points) is more suited to the PCA based system compared to the other techniques (which in effect are "single point to single point" transforms in the PCA based system). For the DCTmod2 based system, the results suggest that the proposed MLS technique (where the shift of the means is found under a maximum likelihood constraint) is more suitable than MLLR (due to a lower number of free parameters) and UBMdiff (due to lack of heuristics). The results further suggest that frontal model augmentation has beneficial effects for both PCA and DCTmod2 based systems. The results also suggest that the standard DCTmod2 based system is less affected by out-of-plane rotations than the corresponding ...

In this work we propose to address the problem of non-frontal face verification when only a frontal training image is available (e.g. a passport photograph) by augmenting a client's frontal face model with artificially synthesized models for non-frontal views. In the framework of a Gaussian Mixture Model (GMM) based classifier, two techniques are proposed for the synthesis: UBMdiff and LinReg. Both techniques rely on a priori information and learn how face models for the frontal view are related to face models at a non-frontal view. The synthesis and augmentation approach is evaluated by applying it to two face verification systems: Principal Component Analysis (PCA) based and DCTmod2 [29] based; the two systems are a representation of holistic and non-holistic approaches, respectively. Results from experiments on the FERET database suggest that in almost all cases, frontal model augmentation has beneficial effects for both systems; they also suggest that the LinReg technique (which is based on multivariate regression of classifier parameters) is more suited to the PCA based system and that the UBMdiff technique (which is based on differences between two general face models) is more suited to the DCTmod2 based system. The results also support the view that the standard DCTmod2/GMM system (trained on frontal faces) is less affected by out-of-plane rotations than the corresponding PCA/GMM system; moreover, the DCTmod2/GMM system using augmented models is, in almost all cases, more robust than the corresponding PCA/GMM system.

We present a novel and computationally fast method for automatic human face authentication. Taking a 3D triangular facial mesh as input, the approach first automatically extracts the bilateral symmetry plane of the face surface. The intersection between the symmetry plane and the facial surface, namely the Symmetry Profile, is then computed. By using both the mean curvature plot of the facial surface and the curvature plot of the symmetry profile curve, three essential points of the nose on the symmetry profile are automatically extracted. The three essential points uniquely determine a Face Intrinsic Coordinate System (FICS). Different faces are aligned based on the FICS. The Symmetry Profile, together with two transverse profiles compose a compact representation, called the SFC representation, of a 3D face surface. The face authentication and recognition steps are finally performed by comparing the SFC representations of the faces. The proposed method was tested on 213 face surfaces, which come from 164 individuals and cover a wide ethnic and age variety and variable facial expressions. The Equal Error Rate (EER) of face authentication on the tested faces is 3.7%; the rank one recognition rate is 90%.

This paper investigates facial expression effects in face recognition from 3D shape using partial ICP. The partial ICP method could implicitly and dynamically extract the rigid parts of facial surface by selecting a part of nearest points pairs to calculate dissimilarity measure during registration of facial surfaces. The method is expected to be able to get much better performance than other methods in 3D face recognition under expression variation for its dynamic extraction of rigid parts of facial surface at the same time of matching. We also present an effective method for coarse alignment of facial shape, which is fully automatic.

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In this paper, we present a method that detects lesions in two-dimensional (2D) cross-sectional brain images. Use of the morphological watershed segmentation technique localizes shape variation in the gray level distribution of brain images and, in turn, identifies the regions with abnormal shape and/or texture structure. The detected brain areas are then subjected to a model-based inverse filtering to determine their physiological characteristics whether they are lesions or other types of anomalies. The proposed algorithm was tested on different images of “The Whole Brain Atlas ” database [13]. The experimental results have produced 90 % classification accuracy in processing 10 arbitrary images, representing different kinds of brain lesion.