FaceCaps for facial expression recognition

Wu, Frederick Y.; Pang, Chaoyi; Zhang, Bailing

doi:10.1002/cav.2021

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…Wang and Zhang proposed CNN with a multidimensional sequence occlusion face feature extraction module and used the deep learning method to improve the recognition rate [28]. Wu et al proposed a novel face recognition network, FaceCaps to provide an efficient method for complex face expression recognition [29]. Zhong et al proposed a face-part attention mechanism-based method to extract features of the whole face image [30].…”

Section: Related Workmentioning

confidence: 99%

“…In face recognition experiments, the commonly used face recognition occlusion datasets FDDB, COFW, and MAFA were selected to complete the test. FaceCaps [29], FPA-FER [30], and DNN-FER [31] were selected for comparison. Simulations were performed on the face detection dataset and benchmark (FDDB), Caltech occluded faces in the wild (COFW), and multiscale attention feature aggregation (MAFA) datasets, respectively, and each group of simulations is carried out for 100 times.…”

Section: Face Recognition Of University Information Servicesmentioning

confidence: 99%

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AI-Based Secure Construction of University Information Services Platform

Wang

2022

Security and Communication Networks

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The service efficiency of the university information services platform directly affects the efficiency of university management. However, the university has rich data resources and complex management, and how to construct a comprehensive, standardized, efficient, and shared university information services platform has become a research hotspot. In recent years, the progress of artificial intelligence (AI) in many aspects has created opportunities for its large-scale application in smart campuses. With the wide penetration of AI technology into all walks of life, especially in the fields of industry, commerce, finance, security, and so on, some technologies have experienced practical tests. Many scholars have discussed the significance and possibility of the application of AI in the field of education from a theoretical level. The university’s secure information services platform can penetrate into all details of university management. Therefore, this paper studies the construction of a university secure information services platform based on AI, taking the dormitory allocation of freshmen and the face recognition of each building as the entry points. First, the beetle antennae search algorithm was introduced to improve the clustering efficiency and accuracy of the K-means algorithm for intelligent dormitory allocation. Then, the improved variational auto encoder-generative adversarial networks (VAE-GAN) model and convolutional neural networks (CNN)-based face recognition algorithm are proposed to enhance the security of building entry in universities. Finally, the simulation results reveal that the proposed two algorithms improve the clustering efficiency in dormitory allocation and the security of the university in the basic construction of the university information services platform, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Face Recognition Of University Information Servicesmentioning

confidence: 99%

AI-Based Secure Construction of University Information Services Platform

Wang

2022

Security and Communication Networks

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“…The identification of facial expressions is important to artificial intelligence and has enormous promise in psychological research, driver fatigue monitoring, interactive game creation, virtual reality [ 45 ], intelligent education [ 46 ], and medical fields [ 47 ]. After recognizing facial expressions, Wu comprehended the emotional content of images and generated image captions using the Face-Cap model [ 48 ]; Cha used surface electromyography (sEMG) around the eyes [ 49 ] (sEMG reference) to react to the user’s facial expressions [ 50 ], thereby performing expression recognition.…”

Section: Biometric Recognition Mechanismmentioning

confidence: 99%

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

Niu

et al. 2023

Micromachines

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Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identif ication speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. This work presents several fingerprint acquisition techniques, such as optical capacitive and ultrasonic, and analyzes acquisition types and structures. In addition, the pros and drawbacks of various sensor types, as well as the limits and benefits of optical, capacitive, and ultrasonic kinds, are discussed. It is the necessary stage for the application of the Internet of Things (IoT).

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“…IACNN [60] 50.98 DLP-CNN [43] 51.05 Island Loss [8] 52.52 TDTLN [61] 53.10 RAN [48] 54.19 LDL-ALSG [56] 56.50 ViT + SE [62] 54.29 FaceCaps [63] 58.50 In addition, the number of attention heads obviously affects the performance of our model. Figure 5 shows the accuracy results with a changing number of attention heads on the RAF-DB dataset.…”

Section: Accuracy (%)mentioning

confidence: 99%

Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition

et al. 2023

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This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be subtle. Secondly, facial expressions simultaneously exhibit themselves through multiple facial regions, and for recognition, a holistic approach by encoding high-order interactions among local features is required. To address these issues, this work proposes DAN with three key components: Feature Clustering Network (FCN), Multi-head Attention Network (MAN), and Attention Fusion Network (AFN). Specifically, FCN extracts robust features by adopting a large-margin learning objective to maximize class separability. In addition, MAN instantiates a number of attention heads to simultaneously attend to multiple facial areas and build attention maps on these regions. Further, AFN distracts these attentions to multiple locations before fusing the feature maps to a comprehensive one. Extensive experiments on three public datasets (including AffectNet, RAF-DB, and SFEW 2.0) verified that the proposed method consistently achieves state-of-the-art facial expression recognition performance. The DAN code is publicly available.

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FaceCaps for facial expression recognition

Cited by 10 publications

References 24 publications

AI-Based Secure Construction of University Information Services Platform

AI-Based Secure Construction of University Information Services Platform

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition

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