Face Recognition using PCA and LDA with Singular Value Decomposition (SVD)
نویسندگان
چکیده
Linear Discriminant Analysis(LDA) is well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as face recognition. In this paper we present a new variant on Linear Discriminant Analysis (LDA) for face recognition by reducing dimensions of input data using matrix representation and after that using singular value decomposition to reduce dimensions of scatter matrix. Experiments on ORL face database shows the effectiveness of our proposed algorithm and results compared with other LDA based methods shows that the proposed scheme gives comparatively better results than previous methods in terms of recognition rate and reduced time complexity.
منابع مشابه
Disguised Face Recognition by Using Local Phase Quantization and Singular Value Decomposition
Disguised face recognition is a major challenge in the field of face recognition which has been taken less attention. Therefore, in this paper a disguised face recognition algorithm based on Local Phase Quantization (LPQ) method and Singular Value Decomposition (SVD) is presented which deals with two main challenges. The first challenge is when an individual intentionally alters the appearance ...
متن کاملFace Recognition Based Rank Reduction SVD Approach
Standard face recognition algorithms that use standard feature extraction techniques always suffer from image performance degradation. Recently, singular value decomposition and low-rank matrix are applied in many applications,including pattern recognition and feature extraction. The main objective of this research is to design an efficient face recognition approach by combining many tech...
متن کاملA Survey: Linear and Nonlinear PCA Based Face Recognition Techniques
Face recognition is considered to be one of the most reliable biometric, when security issues are taken into concern. For this, feature extraction becomes a critical problem. Different methods are used for extraction of facial feature which are broadly classified into linear and nonlinear subspaces. Among the linear methods are Linear Discriminant Analysis (LDA), Bayesian Methods (MAP and ML), ...
متن کاملFractional order singular value decomposition representation for face recognition
Face Representation (FR) plays a typically important role in face recognition and methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been received wide attention recently. However, despite of the achieved successes, these FR methods will inevitably lead to poor classification performance in case of great facial variations such as expression, lighting,...
متن کاملLinear Discriminant Analysis for Subclustered Data
Linear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However, the original LDA has limitations due to the assumption of a unimodal structure for each cluster, which is not satisfied in many applications such as facial image data when variations, e.g. angle and illumination, can significantly influence the images. In this paper, we propose a novel meth...
متن کامل