Automated facial feature detection and face recognition using Gabor features on range and portrait images

Abstract

In this paper, we present a novel identity verification system based on Gabor features extracted from range (3D) representations of faces. Multiple landmarks (fiducials) on a face are automatically detected using these Gabor features. Once the landmarks are identified, the Gabor features on all fiducials of a face are concatenated to form a feature vector for that particular face. Linear discriminant analysis (LDA) is used to reduce the dimensionality of the feature vector while maximizing the discrimination power. These novel features were tested on 1196 range images. The same features were also extracted from portrait images, and the accuracies of both modalities were compared. A superior verification accuracy was obtained using the range data, and a highly competitive accuracy to that of other techniques in the literature was also obtained for the portrait data.

A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion

“…In Wang et al [38], a point signature representation and the Gabor jets from 2D texture images are used to represent the 3D face mesh. Salah and Jahanbin et al [22,39] proposed the Gabor wavelet coefficient so that the local appearance in 2D texture image and local patch in the range data around each landmark can be modeled well. As the same thought, in Lu and Jain [40], the local shape index feature and cornerness texture feature around seven landmarks were computed and fused to detect landmarks jointly.…”

“…During the past decade, more studies about facial landmarks' estimation on 3D facial data have been presented. Most of the approaches [20][21][22] applied both texture data and geometry data to detect landmarks jointly, which can enhance the performance effectively. In fact, not all 3D scanners provide texture and the texture information is not invariant to viewpoint and lighting conditions, so it is necessary to locate landmarks accurately only from 3D geometry data.…”

Object Recognition for Intelligent Horse Riding Simulator

“…The face detection has processed to distinguish face region of a rectangular shape by recognizing a front of face from input image. Face features [5] are extracted from face region by extracting points from eye, nose, lops and face contour. Face are recognized by matching feature vector of exerciser with registered face features in database.…”

“…An automatic personalized healthcare system has motivated by recognizing objects [2][3][4][5][6] such as face and human's posture.…”

An Efficient Face Recognition System based on the Hybridization of Pose Invariant and Illumination Process

“…Face recognition is non-intrusive and it needs no support from the test subjects, whereas supplementary biometrics necessitates a subject's cooperation. In favor of case, inside iris or fingerprint recognition, one be supposed to have a fleeting look over an eye scanner or place their finger on a fingerprint reader [4].…”