Sparse representation for face recognition: A review paper

Madarkar, Jitendra; Sharma, Poonam; Singh, Rimjhim Padam

doi:10.1049/ipr2.12155

Cited by 12 publications

(2 citation statements)

References 64 publications

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“…The baseline network for the ablation experiments still chooses ResNet50 as the base comparison model because the CSINet network proposed in this paper modifies and adds three main modules to ResNet50. In strategy (a), the ResNet50 network obtains recognition accuracies of 76.43% and 79.31% on the data sets RAF-DB and FERPlus, respectively, and in strategy (b), the Attention Residual Module [50] is replaced with the traditional basic residual structure in ResNet50. The recognition accuracies on the data sets are improved by 7.86% and 5.88%, respectively, which is an increase of a large magnitude that indicates that the ARM module improves the ability to extract important features from the bottom layer of the network.…”

Section: Ablation Experimentsmentioning

confidence: 99%

CSINet: Channel–Spatial Fusion Networks for Asymmetric Facial Expression Recognition

Cheng,

Kong

2024

Symmetry

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Occlusion or posture change of the face in natural scenes has typical asymmetry; however, an asymmetric face plays a key part in the lack of information available for facial expression recognition. To solve the problem of low accuracy of asymmetric facial expression recognition, this paper proposes a fusion of channel global features and a spatial local information expression recognition network called the “Channel–Spatial Integration Network” (CSINet). First, to extract the underlying detail information and deepen the network, the attention residual module with a redundant information filtering function is designed, and the backbone feature-extraction network is constituted by module stacking. Second, considering the loss of information in the local key area of face occlusion, the channel–spatial fusion structure is constructed, and the channel features and spatial features are combined to enhance the accuracy of occluded facial recognition. Finally, before the full connection layer, more local spatial information is embedded into the global channel information to capture the relationship between different channel–spatial targets, which improves the accuracy of feature expression. Experimental results on the natural scene facial expression data sets RAF-DB and FERPlus show that the recognition accuracies of the modeling approach proposed in this paper are 89.67% and 90.83%, which are 13.24% and 11.52% higher than that of the baseline network ResNet50, respectively. Compared with the latest facial expression recognition methods such as CVT, PACVT, etc., the method in this paper obtains better evaluation results of masked facial expression recognition, which provides certain theoretical and technical references for daily facial emotion analysis and human–computer interaction applications.

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Section: Ablation Experimentsmentioning

confidence: 99%

CSINet: Channel–Spatial Fusion Networks for Asymmetric Facial Expression Recognition

Cheng,

Kong

2024

Symmetry

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“…The main purpose is to make the recognition effect more obvious [38,39]. With the constraint of R ys , the above formula can reduce the intraclass spacing of face feature samples and adjust the weight of each layer in a direction more conducive to face recognition [40].…”

Section: Fast Recognition Of Face Image Featuresmentioning

confidence: 99%

Research on Fast Face Recognition Method Based on Double Decision Subspace

Wang

Zhang

Huang

2022

Wireless Communications and Mobile Computing

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Because a large number of labeled face data samples in special scenes need a large number of training samples with identity markers, and it is impossible to accurately extract the characteristics of small samples, a fast face recognition method based on double decision subspace is proposed. A feature recognition structure based on double decision subspace is constructed to preprocess the face image and separate the local features of several corresponding face images. The local binary pattern is used to extract the local texture features of the face, and the deep convolution network face fast recognition model is constructed. The convolution network is used to share the weight, pool, and downsampling to reduce the complexity of the model. The constructed recognition model is used to recognize the features of the face image, and the fast face recognition is effectively completed. The experimental results show that the designed method has high recognition accuracy, less average recognition time, and good recognition performance.

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Sparse Representation Based Face Recognition Using VGGFace

Madarkar

Sharma

2021

Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (IC

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Sparse representation for face recognition: A review paper

Cited by 12 publications

References 64 publications

CSINet: Channel–Spatial Fusion Networks for Asymmetric Facial Expression Recognition

CSINet: Channel–Spatial Fusion Networks for Asymmetric Facial Expression Recognition

Research on Fast Face Recognition Method Based on Double Decision Subspace

Sparse Representation Based Face Recognition Using VGGFace

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