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Improved Person Identification System using Face Biometric Detection


Affiliations
1 PG and Research Department of Computer Science, Gobi Arts and Science College (Autonomous), Gobichettipalayam – 638 453, Erode District, Tamil Nadu, India
 

Biometrics is measurable characteristics specific to an individual. Face detection has diverse applications especially as an identification solution which can meet the crying needs in security areas. While traditionally 2D images of faces have been used, 3D scans that contain both 3D data and registered color are becoming easier to acquire. Before 3D face images can be used to identify an individual, they require some form of initial alignment information, typically based on facial feature locations. We follow this by a discussion of the algorithms performance when constrained to frontal images and an analysis of its performance on a more complex dataset with significant head pose variation using 3D face data for detection provides a promising route to improved performance.

Keywords

Biometrics, Face, Face Sensor, Feature Extraction, Template Matching.
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  • Improved Person Identification System using Face Biometric Detection

Abstract Views: 169  |  PDF Views: 5

Authors

A. Dhanalakshmi
PG and Research Department of Computer Science, Gobi Arts and Science College (Autonomous), Gobichettipalayam – 638 453, Erode District, Tamil Nadu, India
B. Srinivasan
PG and Research Department of Computer Science, Gobi Arts and Science College (Autonomous), Gobichettipalayam – 638 453, Erode District, Tamil Nadu, India

Abstract


Biometrics is measurable characteristics specific to an individual. Face detection has diverse applications especially as an identification solution which can meet the crying needs in security areas. While traditionally 2D images of faces have been used, 3D scans that contain both 3D data and registered color are becoming easier to acquire. Before 3D face images can be used to identify an individual, they require some form of initial alignment information, typically based on facial feature locations. We follow this by a discussion of the algorithms performance when constrained to frontal images and an analysis of its performance on a more complex dataset with significant head pose variation using 3D face data for detection provides a promising route to improved performance.

Keywords


Biometrics, Face, Face Sensor, Feature Extraction, Template Matching.