A Supervised Hybrid Methodology for Pose and Illumination Invariant 3D Face Recognition

Abstract

The 2D face recognition systems encounter difficulties in recognizing faces with illumination variations. The depth map of the 3D face data has the potential to handle the variation in illumination of face images. The view variations are handled by using the moment invariants. Moment Invariants are used as rotation invariant features of the face image. For feature matching an efficient fuzzy-neural technique is proposed. The PCA components of normalized depth map and Moment invariants on mesh images are used successfully to implement a fuzzy neural network based fully automatic 3D face recognition system. The system is evaluated on the 3D face databases, the CASIA database. The proposed system provides recognition accuracy that is resulted in to an efficient 3D face recognition system.

Weighted Gradient Feature Extraction Based on Multiscale Sub-Blocks for 3D Facial Recognition in Bimodal Images

“…The proposed LGP-WHGO method was compared with the state-of-art facial recognition methods in two databases. The method in [2] is the global feature-based method, the method in [13] is the local feature-based method, and the methods in [15][16][17][18][19] are multimodal hybrid methods. Tables 7 and 8 are RR comparisons with state-of-art methods on the CASIA and FRGC v2.0 databases, respectively.…”

“…The Bosphorus experimental dataset contains not only yaw rotation face images (yaw 10, 20, 30, and 45 degrees), but also occlusion face images (eye, mouth, eyeglasses, hair occlusion)-with that dataset, the RR reached 96.75%. Methods in [2,17] used the trained advantages of neural networks to extract features and classification. The method in [18] used different combinations of features, including MSLBP, SLF, Gabor wavelets, and SIFT for classification.…”

“…Global feature-based methods often extract statistical features from depth images [1]. Thakare et al [2] used the principal component analysis (PCA) components of the normalized depth image and moment invariants on mesh images to implement an automatic 3D facial recognition system based on the fuzzy neural network (FNN). Independent component analysis (ICA) [3], linear discriminant analysis (LDA) [4], and sparse preserving projection (SPP) [5] are also used to extract global facial features.…”