Cross-illumination Evaluation of Hand Crafted and Deep Features for Fusion of Selfie Face and Ocular Biometrics
Kondapi, Leena Rattani, Ajita Derakhshani, Reza R.
Kondapi, Leena
Rattani, Ajita
Derakhshani, Reza R.
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2020-03-12
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Conference paper
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Keywords
Biometrics,Classification (of information),Large dataset,Light,National security,Security systems
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Citation
L. Kondapi, A. Rattani and R. Derakhshani, "Cross-illumination Evaluation of Hand Crafted and Deep Features for Fusion of Selfie Face and Ocular Biometrics," 2019 IEEE International Symposium on Technologies for Homeland Security (HST), Woburn, MA, USA, 2019, pp. 1-4
Abstract
This paper addresses the implementation of a multiunit biometric system. Results are shown for multi-unit classification with VISible light mobile Ocular Biometric (VISOB) dataset using feature descriptors such as Local Binary Patterns (LBP) and Histogram of oriented gradients (HOG). We also evaluate the pre-trained deep learning models such as VGG16, ResNet18, MobileNetV1, MobileNetV2, and LightCNN9. Experimental evaluation on large scale VISOB dataset suggests that feature-level fusion followed by score-level fusion of left ocular region, right ocular region and face region in office light condition, daylight and dims condition has provided Equal Error Rates (EER) of 9.3%, 8.0% and 10.6% respectively. Also, combining the pretrained models using feature fusion decreased the EER even further.
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IEEE
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IEEE International Symposium on Technologies for Homeland Security (HST);2019