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The performance of these selected Discriminative ZMs (DZMs) and Discriminative PZMs (DPZMs) features have been compared to that of their corresponding conventional approaches on YALE, ORL and FERET databases against illumination, expression, scale and pose variations. An extension to these DZMs and DPZMs have been proposed by combining them with PCA and FLD. It has been observed from the exhaustive experimentation that the recognition rate is improved by 2-6%, at reduced dimensions and with less computational complexity, than that of using the successive ZMs and PZMs features.<\/p>","DOI":"10.4018\/ijcvip.2012040102","type":"journal-article","created":{"date-parts":[[2012,10,22]],"date-time":"2012-10-22T12:06:06Z","timestamp":1350907566000},"page":"12-35","source":"Crossref","is-referenced-by-count":7,"title":["Discriminative Zernike and Pseudo Zernike Moments for Face Recognition"],"prefix":"10.4018","volume":"2","author":[{"given":"Chandan","family":"Singh","sequence":"first","affiliation":[{"name":"Punjabi University, Patiala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekta","family":"Walia","sequence":"additional","affiliation":[{"name":"South Asian University, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neerja","family":"Mittal","sequence":"additional","affiliation":[{"name":"CSIR - Central Scientific Instruments Organisation, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijcvip.2012040102-0","unstructured":"AT&T Laboratories Cambridge. 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