Eleyan, A.Köse, KıvançÇetin, A. Enis2016-02-082016-02-0820142194-5357http://hdl.handle.net/11693/26986Date of Conference: 11-13 September 2013Conference Name: 5th International Conference on Image Processing and Communications, IP&C 2013In this paper a new approach for image feature extraction is presented. We used the Compressive Sensing (CS) concept to generate the measurement matrix. The new measurement matrix is different from the measurement matrices in literature as it was constructed using both zero mean and nonzero mean rows. The image is simply projected into a new space using the measurement matrix to obtain the feature vector. Another proposed measurement matrix is a random matrix constructed from binary entries. Face recognition problem was used as an example for testing the feature extraction capability of the proposed matrices. Experiments were carried out using two well-known face databases, namely, ORL and FERET databases. System performance is very promising and comparable with the classical baseline feature extraction algorithms.EnglishMeasurement matrixCompressed sensingFace imageFace recognitionFeature extractionSignal reconstructionVector spacesConference Paper10.1007/978-3-319-01622-1_21