Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Abstract

Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.

Efficient human face recognition in real-life applications using the discrete wavelet transformation (HFRDWT)

“…Some face recognition algorithms can identify facial features by obtaining landmarks from a face image, such as eyes, noses, and lips, and detect the distance between them in order to encode the discriminative information as a consolidated features vector [60]. These algorithms include -but are not limited to -: Local Binary Patterns (LBP) [24], Haar features Transform [50], Histogram of oriented gradients [19] (HOG), Principal Component Analysis (PCA) [27], Scale Invariant Feature Transform (SIFT) [8], and Speeded Up Robust Features (SURF) [59]. Regarding classification algorithms, one might use many of them; for example, Deep Neural Networks, Support Vector Machines (SVM), and decision trees [63].…”