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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.