Efficient Hybrid Descriptor for Face Verification in the Wild Using the Deep Learning Approach
Autor: | Mebarka Belahcene, Ahmed Ben Hamida, Bilel Ameur, Sabeur Masmoudi |
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Rok vydání: | 2019 |
Předmět: |
General Computer Science
business.industry Computer science Deep learning Process (computing) Pattern recognition 02 engineering and technology 01 natural sciences Convolutional neural network Field (computer science) Electronic Optical and Magnetic Materials Image (mathematics) 010309 optics Support vector machine ComputingMethodologies_PATTERNRECOGNITION Face (geometry) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business |
Zdroj: | Optical Memory and Neural Networks. 28:151-164 |
ISSN: | 1934-7898 1060-992X |
Popis: | In this work, we propose a novel model-based on a new Deep Hybrid Descriptor learning called DeepGLBSIF (Gabor Local Binarized Statistical Image Feature) for effective extraction and over-complete features in multilayer hierarchy. The typology of our methodology is the same as that of Convolutional Neural Network (CNN) which is one of the intensively-applied deep learning architectures. This field was developed due to: (i) end-to-end learning of the process utilizing a convolutional neural network (CNN), and (ii) the presence of very wide training databases. Our method allows improving the use of the interactions between global and local features for the model, which allowed providing effective and discriminating representations. In our study, the trainable kernels were substituted by our hybrid descriptor GLBSIF. Thus, the developed DeepGLBSIF architecture was efficiently and simply constructed and learned for Face Verification in the Wild. Finally, the classification process was carried out by applying distance measure Cosine and Support Vector Machine (SVM). Our experiments were performed on three large, real-world face datasets: LFW, PubFig and VGGface2. Experimental results demonstrate that our DeepGLBSIF approach provided competitive performance, compared to the others presented in state-of-the-art based on the LFW dataset for facial verification. A public CASIA-WebFace database was utilized in the training step of the introduced approach. |
Databáze: | OpenAIRE |
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