Classifying Faces with Non-negative Matrix Factorization
نویسندگان
چکیده
This paper addresses the well-known problem of recognizing faces under several unfavorable situations. We have analyzed situations with changes in expression, in illumination and occlusions such as faces wearing sunglasses or scarfs. We have introduced the use of the Non-negative Matrix Factorization (NMF) technique in the context of classification of face images and we have directly compared performances of NMF and Principal Component Analysis (PCA) using a well-known face database, the AR, that contains a large number of individuals taken under several conditions. Moreover, these results have also been compared to two leading algorithms, one template based and the other feature based, noticing that NMF is able to improve them when using a high dimensional space. In addition, NMF has been used with some distance metrics as L1, L2 or correlation in order to determine the best one for such problem. We have discovered that the correlation metric is the most suitable one for our problem.
منابع مشابه
Iterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...
متن کاملA new approach for building recommender system using non negative matrix factorization method
Nonnegative Matrix Factorization is a new approach to reduce data dimensions. In this method, by applying the nonnegativity of the matrix data, the matrix is decomposed into components that are more interrelated and divide the data into sections where the data in these sections have a specific relationship. In this paper, we use the nonnegative matrix factorization to decompose the user ratin...
متن کاملA Multiscale Approach for Nonnegative Matrix Factorization with Applications to Image Classification
We use a multiscale approach to reduce the time to produce the nonnegative matrix factorization (NMF) of a matrix A, that is, A ≈ WH. We also investigate QR factorization as a method for initializing W during the iterative process for producing the nonnegative matrix factorization of A. Finally, we use our approach to produce nonnegative matrix factorizations for classifying images and compare ...
متن کاملVoice-based Age and Gender Recognition using Training Generative Sparse Model
Abstract: Gender recognition and age detection are important problems in telephone speech processing to investigate the identity of an individual using voice characteristics. In this paper a new gender and age recognition system is introduced based on generative incoherent models learned using sparse non-negative matrix factorization and atom correction post-processing method. Similar to genera...
متن کاملNon-negative matrix factorization for visual coding
This paper combines linear sparse coding and nonnegative matrix factorization into sparse non-negative matrix factorization. In contrast to non-negative matrix factorization, the new model can leam much sparser representation via imposing sparseness constraints explicitly; in contrast to a close model non-negative sparse coding, the new model can learn parts-based representation via fully multi...
متن کامل