ORB-PCA Based Feature Extraction Technique for Face Recognition
نویسندگان
چکیده
منابع مشابه
Hybrid Feature Extraction Technique for Face Recognition
This paper presents novel technique for recognizing faces. The proposed method uses hybrid feature extraction techniques such as Chi square and entropy are combined together. Feed forward and self-organizing neural network are used for classification. We evaluate proposed method using FACE94 and ORL database and achieved better performance. Keywords-Biometric; Chi square test; Entropy; FFNN; SOM.
متن کاملGamma Correction Technique Based Feature Extraction for Face Recognition System
One of the most important challenges for practical face recognition systems is to make recognition more reliable under uncontrolled lighting conditions. We tackle this by using novel illumination-insensitive preprocessing method. The proposed face recognition system consists of a gamma correction, a preprocessing stage, a hybrid Fourier-based facial feature extraction, and Principal Component L...
متن کاملCombining Block-based PCA, Global PCA and LDA for Feature Extraction in Face Recognition
This paper presents a novel methodology for pattern classification, combining different feature extraction approaches, more precisely, global PCA, block-based PCA and LDA in order to improve the face classification performance. Block-based PCA is a new technique that is being widely used for feature extraction, especially in face recognition. Basically, the idea of block-based PCA is to first d...
متن کاملSVM-based feature extraction for face recognition
The primary goal of linear discriminant analysis (LDA) in face feature extraction is to find an effective subspace for identity discrimination. The introduction of kernel trick has extended the LDA to nonlinear decision hypersurface. However, there remained inherent limitations for the nonlinear LDA to deal with physical applications under complex environmental factors. These limitations includ...
متن کاملTwo-Dimensional Heteroscedastic Feature Extraction Technique for Face Recognition
One limitation of vector-based LDA and its matrix-based extension is that they cannot deal with heteroscedastic data. In this paper, we present a novel two-dimensional feature extraction technique for face recognition which is capable of handling the heteroscedastic data in the dataset. The technique is a general form of two-dimensional linear discriminant analysis. It generalizes the interclas...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2015
ISSN: 1877-0509
DOI: 10.1016/j.procs.2015.08.080