LBP-like feature based on Gabor wavelets for face recognition

Li, Wenjuan; Wang, Jun; Huang, Zheng-Hai; Zhang, Ting; Du, Daniel K.

doi:10.1142/s0219691317500497

Cited by 8 publications

(6 citation statements)

References 39 publications

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“…Since then, many extended variations of Gabor wavelets have delivered significant improvement in recognition accuracy in many extensive works on implementation in face recognition recently such as in [6, 14–16]. These findings and results have showcased Gabor face descriptor as a superior descriptor for face recognition.…”

Section: Introductionmentioning

confidence: 96%

Locally lateral manifolds of normalised Gabor features for face recognition

Zaman

2020

IET Computer Vision

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Due to inherent characteristics of multiscale and orientation, normalised Gabor features have been successfully used in face recognition. Various previous works have showcased the strength and feasibility of this approach, especially on its robustness against local variations. However, the projected features are numerous and substantial in dimension, which is largely due to the convolution of multiscale and orientation of wavelets. Such features, when used in practical face recognition, would require relatively lengthy classification process, particularly when it involves computationally extensive local classifier or experts, such as ensembles of local cosine similarity (ELCS) classifier. The authors address this issue by simultaneously reducing the size of Gabor features laterally and locally using a manifold learning method called locally linear embedding (LLE). This method is thus denoted as locally lateral normalised local Gabor feature vector with LLE (LGFV/LN/LLE). Results on several publicly available face datasets reveal the superiority of the authors’ approach in terms of improvements in feature compression of LGFV features by up to a reduction of 95% of total dimensionality while increasing the average classification accuracy by 26%. Altogether, the authors show that their LGFV/LN/LLE augmented by ELCS classifiers delivers equivalent result when compared against the state‐of‐the‐art.

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Section: Introductionmentioning

confidence: 96%

Locally lateral manifolds of normalised Gabor features for face recognition

Zaman

2020

IET Computer Vision

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“…e common methods of appearance expression include contour template, light flow, and feature point. In the process of candidate region video image processing and feature extraction, color histogram, Haar feature or Haar-like feature, HOG feature operator [11], and wavelet algorithm [12] are usually used. en, machine learning classifiers, such as Softmax and SVM, and boosting or random forest classification algorithms [13][14][15], are used.…”

Section: Introductionmentioning

confidence: 99%

A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model

Shi

Tang²

2022

Journal of Electrical and Computer Engineering

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Deep learning theory is widely used in face recognition. Combined with the needs of classroom attendance and students’ learning status monitoring, this article analyzes the YOLO (You Only Look Once) face recognition algorithms based on regression method. Aiming at the problem of small target missing detection in the YOLOv3 network structure, an improved YOLOv3 algorithm based on Bayesian optimization is proposed. The algorithm uses deep separable convolution instead of conventional convolution to improve the Darknet-53 basic network, and it reduces the amount of calculation and parameters of the network. A multiscale feature pyramid is built, and an attention guidance module is designed to strengthen multiscale fusion, detecting different sizes of targets. The loss function is improved to solve the imbalance of positive and negative sample distribution and the imbalance between simple samples and difficult samples. The Bayesian function is adopted to optimize the classifier and improve the classification efficiency and accuracy, ensuring the accuracy of small target detection. Five groups of comparative experiments are carried out on public COCO and VOC2012 datasets and self-built datasets. The experimental results show that the proposed improved YOLOv3 model can effectively improve the detection accuracy of multiple faces and small targets. Compared with the traditional YOLOv3 model, the mean mAP of the target is improved by more than 1.2%.

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“…e common appearance expression methods include contour template, optical flow, and feature points. Generally, color histogram, Haar feature or Haar-like feature, histogram of oriented gradient (HOG) feature operator [15], and wavelet algorithm [16] are used to extract feature from the candidate region of video image. en, machine learning classifiers such as softmax, SVM, boosting, or random forest are used for fast classifier learning.…”

Section: Introductionmentioning

confidence: 99%

Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network

Shi

Tang²

2021

Journal of Electrical and Computer Engineering

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The rapid development of transportation industry has brought some potential safety hazards. Aiming at the problem of driving safety, the application of artificial intelligence technology in safe driving behavior recognition can effectively reduce the accident rate and economic losses. Based on the presence of interference signals such as spatiotemporal background mixed signals in the driving monitoring video sequence, the recognition accuracy of small targets such as human eyes is low. In this paper, an improved dual-stream convolutional network is proposed to recognize the safe driving behavior. Based on convolutional neural networks (CNNs), attention mechanism (AM) is integrated into a long short-term memory (LSTM) neural network structure, and the hybrid dual-stream AM-LSTM convolutional network channel is designed. The spatial stream channel uses the CNN method to extract the spatial characteristic value of video image and uses pyramid pooling instead of traditional pooling, normalizing the scale transformation. The time stream channel uses a single-shot multibox detector (SSD) algorithm to calculate the adjacent two frames of video sequence for the detection of small objects such as face and eyes. Then, AM-LSTM is used to fuse and classify dual-stream information. The self-built driving behavior video image set is built. ROC, accuracy rate, and loss function experiments are carried out in the FDDB database, VOT100 data set, and self-built video image set, respectively. Compared with CNN, SSD, IDT, and dual-stream recognition methods, the accuracy rate of this method can be improved by at least 1.4%, and the average absolute error in four video sequences can be improved by more than 2%. On the contrary, in the self-built image set, the recognition rate of doze reaches 68.3%, which is higher than other methods. The experimental results show that this method has good recognition accuracy and practical application value.

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LBP-like feature based on Gabor wavelets for face recognition

Cited by 8 publications

References 39 publications

Locally lateral manifolds of normalised Gabor features for face recognition

Locally lateral manifolds of normalised Gabor features for face recognition

A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model

Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network

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