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Image processing and face detection analysis on face verification based on the age stages
Syambas N.R.a, Purwanto U.H.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]The availability of computer systems has created a variety of automated applications in personal identification. From the various characteristics of biometrics, face recognition techniques mainly face verification has become an area of active research and the application are important in law enforcement because it can be done without involving the subject. However, the influence of age progression on face verification become a challenge to determine the similarity of image pairs from individual faces considering very limited of data base availability. This paper focuses on the development of image processing and face detection on face verification system by improving the image quality. The research use computer simulation, comparative studies, and analytical studies. The simulation of image processing using PhotoScape v3.5 software, while the verification using VeriLook 5.0/MegaMatcher VeriLook 4.0 Algorithm Demo. In the early step, database of face images are grouped by the age stages of education, then the simulation process includes the process of image processing, the enrolment process, and the verification process. The simulation results show the combination of image processing with contrast and sharpen increase the enrolment process by 17.14% with maximum success rate reaches 100%, where the entire 70-images training enrolled successfully. In verification process, the results of the combination increase the success rate of matching faces by 13.57%, where 96 image pairs successfully matched from a total of 140. © 2012 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Age progression,Automated applications,Comparative studies,Face images,Face recognition technique,Face Verification,Face verification systems,Image pairs,Personal identification,Simulation process,Verification process[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]biometrics,face recognition,face verification,image processing,law enforcement[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/TSSA.2012.6366070[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]