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Endra Oey

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Papers by Endra Oey

Research paper thumbnail of Face Verification by Using Sparse Representation Algorithm in Compressive Sensing

 Abstract— Face verification, a part of security systems, is widely used in many applications. T... more  Abstract— Face verification, a part of security systems, is widely used in many applications. This biometric application is more hygienic comparing with other biometric systems since there is no direct contact between face and camera. Moreover, it is a low cost setup. A sparse representation algorithm as a part of compressive sensing was used in this paper with the accuracy achieved up to 88% during non-optimized sensing matrix and with the average time process of 4.37 seconds. The accuracy achieved was 94% during optimized sensing matrix but the average time process was slower at 8.73 seconds. Encryption process also happened during the image compression which not only reduced the size of the image but also increased the data security.

Research paper thumbnail of Projection Matrix Design for Co-Sparse Analysis Model Based Compressive Sensing

MATEC Web of Conferences, 2018

Research paper thumbnail of Projection Matrix Design for Co-Sparse Analysis Model Based Compressive Sensing

MATEC Web of Conferences, 2018

Research paper thumbnail of Compressive sensing using optimized sensing matrix for face verification

IOP Conference Series: Earth and Environment, 2017

Research paper thumbnail of Projection matrix design for compressive sensing

2014 Makassar International Conference on Electrical Engineering and Informatics (MICEEI), 2014

Research paper thumbnail of Face Verification by Using Sparse Representation Algorithm in Compressive Sensing

 Abstract— Face verification, a part of security systems, is widely used in many applications. T... more  Abstract— Face verification, a part of security systems, is widely used in many applications. This biometric application is more hygienic comparing with other biometric systems since there is no direct contact between face and camera. Moreover, it is a low cost setup. A sparse representation algorithm as a part of compressive sensing was used in this paper with the accuracy achieved up to 88% during non-optimized sensing matrix and with the average time process of 4.37 seconds. The accuracy achieved was 94% during optimized sensing matrix but the average time process was slower at 8.73 seconds. Encryption process also happened during the image compression which not only reduced the size of the image but also increased the data security.

Research paper thumbnail of Projection Matrix Design for Co-Sparse Analysis Model Based Compressive Sensing

MATEC Web of Conferences, 2018

Research paper thumbnail of Projection Matrix Design for Co-Sparse Analysis Model Based Compressive Sensing

MATEC Web of Conferences, 2018

Research paper thumbnail of Compressive sensing using optimized sensing matrix for face verification

IOP Conference Series: Earth and Environment, 2017

Research paper thumbnail of Projection matrix design for compressive sensing

2014 Makassar International Conference on Electrical Engineering and Informatics (MICEEI), 2014

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