Local intensity area descriptor for facial recognition in ideal and noise conditions

Tran, Chi-Kien; Tseng, Chin‐Dar; Chao, Pei‐Ju; Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Lee, Tsair-Fwu

doi:10.1117/1.jei.26.2.023011

Cited by 8 publications

(9 citation statements)

References 21 publications

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“…Generally, the keypoint is coupled with a descriptor. In this work, a sparse descriptor, which first detects the points of interest in a given image and then samples a local patch and describes its invariant features [28], is employed to produce the keypoint.…”

Section: Keypoint Detectionmentioning

confidence: 99%

Local keypoint-based Faster R-CNN

Ding

Cheng

et al. 2020

Appl Intell

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Region-based Convolutional Neural Network (R-CNN) detectors have achieved state-ofthe-art results on various challenging benchmarks. Although R-CNN has achieved high detection performance, the research of local information in producing candidates is insufficient. In this paper, we design a Keypoint-based Faster R-CNN (K-Faster) method for object detection. K-Faster incorporates local keypoints in Faster R-CNN to improve the detection performance. In detail, a sparse descriptor, which first detects the points of interest in a given image and then samples a local patch and describes its invariant features, is first employed to produce keypoints. All 2-combinations of the produced keypoints are second selected to generate keypoint anchors, which are helpful for object detection. The heterogeneously distributed anchors are then encoded in feature maps based on their areas and center coordinates. Finally, the keypoint anchors are coupled with the anchors produced by Faster R-CNN, and the coupled anchors are used for Region Proposal Network (RPN) training. Comparison experiments are implemented on PASCAL VOC 07/12 and MS COCO. The experimental results show that our K-Faster approach not only increases the mean Average Precision (mAP) performance but also improves the positioning precision of the detected boxes.

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Section: Keypoint Detectionmentioning

confidence: 99%

Local keypoint-based Faster R-CNN

Ding

Cheng

et al. 2020

Appl Intell

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“…In order to compare the efficiency of proposed and conventional methods, LBP [11] descriptor was used to represent the face image and histogram-based feature was extracted from obtained images. The chi-square distance [6], [11], [12] was chosen for nearest neighbor classifier. The conventional method (CM) is a face recognition method using nearest neighbor classifier.…”

Section: B Experimental Settingsmentioning

confidence: 99%

Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model

Tran¹

2019

IJMLC

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Similarity feature-based selection and classification (SFSC) algorithms, introduced by Tran et al. in 2013, have been used as a tool to reduce storage cost and increase performance of face recognition systems. However, these still exist a problem when automatically selecting a suitable threshold. This paper introduces a new approach, which combines SFSC algorithms, and a wrapper model, to automatically select a suitable threshold and improve face recognition accuracy. The training face image set (which is split into two separated subsets including a training subset and a wrapper subset) is utilized as data input for the similarity feature-based selection algorithm in combination with the wrapper model to identify a best feature set. The obtained feature set will be used for classification. The experiments were conducted on the histogram-based feature and two databases, ORL database of faces and Georgia Tech face database. The results demonstrated that the proposed algorithm not only allowed for automatic detection of the suitable feature set, but also achieved a better recognition accuracy compared to conventional algorithms.

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“…For example, the process for 1-N-N to make a classification is as follows: in order to classify x from the test data, it will find its closest neighbour within the training data, labelling it x^i and then assign x the value of x^i [17]. Works such as Guru et al [17] and Tran et al [18] show us that the k-N-N classifier is a useful function for benchmarking the performance of feature extraction and recognition systems. The k-N-N classifier was also selected because it is a simple to configure, parameter free classifier, that can efficiently test the quality of features.…”

Section: A K-n-n Classifiermentioning

confidence: 99%

Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection

Robson

Zhang

2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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Particle Swarm Optimization (PSO) has become a popular method of feature selection in classification problems, due to its powerful search capability and computational simplicity. Classification problems, such as facial emotion recognition, often involve data sets containing high volumes of features, not all of which are useful for classification. Redundant and irrelevant features have the potential to negatively impact the performance and accuracy of facial emotion recognition systems. The feature selection process identifies the most relevant features to achieve improved classification performance. While the use of PSO as a feature selection method in facial emotion recognition systems has seen some successes, it is still susceptible to the issue of premature convergence. This work presents seven PSO variants which mitigate against the premature convergence problem through the incorporation of three random probability distributions (Cauchy, Gaussian and Lévy). At each iteration of the proposed PSO models, probability distributions are used to increase search diversity and reduce the number of redundant features used for classification. The seven PSO variants presented in this study have demonstrated positive results when tested on real world data sets, outperforming the standard PSO model and other related work within the field.

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Local intensity area descriptor for facial recognition in ideal and noise conditions

Cited by 8 publications

References 21 publications

Local keypoint-based Faster R-CNN

Local keypoint-based Faster R-CNN

Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model

Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection

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