Name
Abstract | ||
|---|---|---|
JPEG compression standard is widely used for reducing the volume of images that are stored or transmitted via networks. In biometrics datasets, facial images are usually stored in JPEG compressed format and should be fully decompressed to be used in a face recognition system. Recently, in order to reduce the computational complexity of JPEG decompression step, face recognition in compressed domain is considered as an emerging topic in face recognition systems. In this paper, a novel coefficient selection method based on face segmentation has been proposed for selecting a limited number of zigzag scanned quantized coefficients in JPEG compressed domain, which led to an improvement in recognition accuracy and a reduction in computational complexity of the face recognition system. In the proposed method, different low frequency coefficients based on the importance of the regions of a face have been selected for recognition process. The experiments were conducted on FERET and FEI face databases, and PCA and ICA methods have been utilized to extract the features of the selected coefficients. Different criteria including recognition accuracy and time complexity metrics were employed in order to evaluate the performance of the proposed method, and the results have been compared with those of the state-of-the art methods. The results show the superiority of the proposed approach, in terms of recognition ranks, discriminatory power and time complexity aspects. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s11760-013-0492-8 | Signal, Image and Video Processing |
Keywords | Field | DocType |
Face recognition, JPEG compressed domain, Coefficient selection, Face database, Feature extraction | Facial recognition system,Computer vision,Lossless JPEG,Pattern recognition,Segmentation,Computer science,Feature extraction,JPEG,Artificial intelligence,Biometrics,Time complexity,Computational complexity theory | Journal |
Volume | Issue | ISSN |
9 | 3 | 1863-1711 |
Citations | PageRank | References |
2 | 0.36 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Shahram Moin | 1 | 10 | 2.12 |
| Alireza Sepas-Moghaddam | 2 | 123 | 12.15 |