3D Angle Searching System with PSO for Face Recognition
نویسندگان
چکیده
Conventional 2D face recognition methods often struggle when a subject's head is turned even slightly to the side. In this study, a face recognition system based on 3D head modeling that is able to tolerate facial rotation angles was constructed by leveraging the Open source graphic library (OpenGL) framework. To minimize the extensive angle searching time that often occurs in conventional 3D modeling, Particle Swarm Optimization (PSO) was used to determine the correct facial angle in 3D. This reduced the angle computation time to 6 seconds, which is significantly faster than other methods. Experimental results showed that successful ID recognition can be achieved with a high recognition rate of 90%.
منابع مشابه
Hybridization of Facial Features and Use of Multi Modal Information for 3D Face Recognition
Despite of achieving good performance in controlled environment, the conventional 3D face recognition systems still encounter problems in handling the large variations in lighting conditions, facial expression and head pose The humans use the hybrid approach to recognize faces and therefore in this proposed method the human face recognition ability is incorporated by combining global and local ...
متن کامل3D Face Recognition using Patch Geodesic Derivative Pattern
In this paper, a novel Patch Geodesic Derivative Pattern (PGDP) describing the texture map of a face through its shape data is proposed. Geodesic adjusted textures are encoded into derivative patterns for similarity measurement between two 3D images with different pose and expression variations. An extensive experimental investigation is conducted using the publicly available Bosphorus and BU-3...
متن کاملPreserving Complete Subspace Structure Projection for Face Recognition
Subspace-based face recognition is one of the most successful methods for face recognition. Eigenfaces, Fisherfaces, and Laplacianfaces methods, which are based on PCA, LPP and LDA that preserve global, local and cluster structure information respectively, are three representative methods of subspace-based face recognition approaches. In this paper, we propose a novel pattern classification nam...
متن کامل3d Face Recognition from Range Image
MIAO, SHUN. 3D Face Recognition From Range Image. (Under the direction of Professor Hamid Krim). In this thesis, we explore the statistical and geometrical behavior of the uncontrolled parameters of a human face, including both the rigid transform caused by a head pose and the non-rigid transform caused by a facial expression. We focus on developing a 3D facial recognition scheme which is robus...
متن کاملPose Normalization via Learned 2D Warping for Fully Automatic Face Recognition
We present a novel approach to pose-invariant face recognition that handles continuous pose variations, is not database-specific, and achieves high accuracy without any manual intervention. Our method uses multidimensional Gaussian process regression to learn a nonlinear mapping function from the 2D shapes of faces at any non-frontal pose to the corresponding 2D frontal face shapes. We use this...
متن کامل