Local binary pattern network: A deep learning approach for face recognition

Abstract

Deep learning is well known as a method to extract hierarchical representations of data. This method has been widely implemented in many fields , including image classification, speech recognition, natural language processing, etc. Over the past decade, deep learning has made a great progress in solving face recognition problems due to its effectiveness. In this thesis a novel deep learning multilayer hierarchy based methodology, named Local Binary Pattern Network (LBPNet), is proposed. Unlike the shallow LBP method, LBPNet performs multi-scale analysis and gains high-level representations from low-level overlapped features in a systematic manner. The LBPNet deep learning network is generated by retaining the topology of Convolutional Neural Network (CNN) and replacing its trainable kernel with the offthe-shelf computer vision descriptor, the LBP descriptor. This enables LBPNet to achieve a high recognition accuracy without requiring costly model learning approach on massive data. LBPNet progressively extracts features from input images from test and training data through multiple processing layers, pairwisely measures the similarity of extracted features in regional level , and then performs the classification based on the aggregated similarity values. Through extensive numerical experiments using the popular benchmarks (i.e., FERET, LFW and YTF) , LBPNet has shown the promising results. Its results outperform (on FERET) or are comparable (on LFW and FERET) to other methods in the same categories, which are single descriptor based unsupervised learning methods 7 Conclusions and Future Work 67 7.1 Conclusions 7.2 Future Work .

Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

“…This is based on the general PCA with the Eigen face concept [17]. For feature extraction, proposed deep learning with optimization algorithm called local binary pattern network enhanced with PSO [18]. The third phase is classification, for the deep learning algorithm called deep belief network is used.…”

Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

“…These algorithms were chosen as they require less processing time for implementation. Furthermore, both have been shown to be feasible for implementation in a real-time environment and produced good recognition results [14][15][16].…”

PyTorch Operations Based Approach for Computing Local Binary Patterns

“…In computer vision applications, one of the important feature extraction approaches is the LBP transform (Pietikäinen et al 2011;Pietikäinen 2010). Due to efficient and computationally lightweight structure, it has been used in various machine learning applications such as LBP network for face recognition (Xi et al 2016), facial expression recognition (Rahul, Kohli, and Agarwal 2020), image forgery detection (Alahmadi et al 2017), acoustic scene classification (Yang and Krishnan 2017), dermoscopic skin lesion image segmentation (Pereira et al 2020), Hyperspectral image classification (Tu et al 2019), on-road vehicle detection in urban traffic scene (Hassaballah, Kenk, and El-Henawy 2020).…”