Face Recognition

Face Detection (Face Recognition Techniques) Part 3

Cascade of Strong Classifiers A boosted strong classifier effectively eliminates a large portion of nonface subwindows while maintaining a high detection rate. Nonetheless, a single strong classifier may not meet the requirement of an extremely low false alarm rate (for example, 10-6 or even lower). A solution is to arbitrate between several detectors (strong classifier) […]

Facial Landmark Localization (Face Recognition Techniques) Part 1

Introduction 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 […]

Facial Landmark Localization (Face Recognition Techniques) Part 2

Global Optimization Facial landmark localization aims to find the best fit of the 88 points in the novel face image with the 2D face model. A new shape X could be obtained with geometry parameter a and shape parameter vector b. For each landmark, a random forest classifier gives the result measuring the distance of […]

Face Tracking and Recognition in Video (Face Recognition Techniques) Part 1

Introduction Faces are expressive three dimensional objects. Information useful for recognition tasks can be found both in the geometry and texture of the face and also facial motion. While geometry and texture together determine the ‘appearance’ of the face, motion encodes behavioral cues such as idiosyncratic head movements and gestures which can potentially aid in […]

Face Tracking and Recognition in Video (Face Recognition Techniques) Part 2

Results for Database-0 We now consider affine transformation. Specifically, the motion is characterized byare deformation parameters and are 2D translation parameters. It is a reasonable approximation because there is no significant out-of-plane motion as the subjects walk toward the camera. Regarding the photometric transformation, only the zero-mean-unit-variance operation is performed to compensate partially for contrast […]

Face Tracking and Recognition in Video (Face Recognition Techniques) Part 3

Face Tracking from Multi-view Videos The tracker is set in a Sequential Importance Resampling (SIR) (particle filtering) framework, which can be broken down into a description of its state space, the state transition model and the observation model. To fully describe the position and pose of a 3D object, we usually need a 6-D representation […]

Face Recognition at a Distance (Face Recognition Techniques) Part 1

Introduction Face recognition, and biometric recognition in general, have made great advances in the past decade. Still, the vast majority of practical biometric recognition applications involve cooperative subjects at close range. Face Recognition at a Distance (FRAD) has grown out of the desire to automatically recognize people out in the open, and without their direct […]

Face Recognition at a Distance (Face Recognition Techniques) Part 2

NFOV Resource Allocation Face capture with active cameras is faced with the problem of resource allocation. Given a limited number of NFOV cameras and a large number of potential targets, it becomes necessary to predict feasible periods of time in the future, during which a target could be captured by a NFOV camera at the […]

Face Recognition at a Distance (Face Recognition Techniques) Part 3

Experiments Our experiments are conducted on a subset of the ND1 face database [10], which contains 534 images from 200 subjects. Regarding the fitting performance measurement, we use the convergence rate (CR) with respect to different levels of perturbation on the initial landmark locations. The fitting is converged if the average mean squared error between […]

Face Recognition Using Near Infrared Images (Face Recognition Techniques) Part 1

Introduction Face recognition should be based on intrinsic factors of the face, such as, the 3D shape and the albedo of the facial surface. Extrinsic factors that include illumination, eyeglasses, and hairstyle, are irrelevant to biometric identity, and hence their influence should be minimized. Out of all these factors, variation in illumination is a major […]

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