Conference proceeding
Soft biometric thermal face recognition using FWT and LDA feature extraction method with RBM DBN and FFNN classifier algorithms
2017 Fourth International Conference on Image Information Processing (ICIIP), v 2018-, pp 1-6
Dec 2017
Abstract
This paper deals with error reduction, in thermal face recognition, by using multi biometrics to improve accuracy. To tackle this, images from the Terravic Facial Infrared Database, have been used with Fast Wavelet Transform (FWT) image compression approach, Linear Discriminant Analysis (LDA) technique, for feature extraction, and Restricted Boltzmann Machines (RBM), Deep Belief Network (DBN) and Feed Forward Neural Network (FFNN) for pre-training, testing and classification. To learn four different sets of training data, categorized using semantics such as: plain faces, faces in glasses, faces in a head gear and faces with facial hairs. These where used to classify the images, and a classification error of 0.0268, 0.030310, 0.02381 and 0.024629 was achieved by the algorithm on the plain faces, glass faces, head gear faces and facial hair faces respectively. By comparing the classification errors across the 4 algorithms, using test images not in the training set, soft-biometric recognition, such as plain face, face in glasses, hairy face and face in a head gear was possible for the thermal images.
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Details
- Title
- Soft biometric thermal face recognition using FWT and LDA feature extraction method with RBM DBN and FFNN classifier algorithms
- Creators
- Evan Hurwitz - University of JohannesburgAli N. Hasan - University of JohannesburgChigozie Orji - University of Johannesburg
- Publication Details
- 2017 Fourth International Conference on Image Information Processing (ICIIP), v 2018-, pp 1-6
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Web of Science ID
- WOS:000428329100124
- Scopus ID
- 2-s2.0-85046963705
- Other Identifier
- 991022004201104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology