Facial expression recognition using optimized active regions

Sun, Aixin; Li, Yingjian; Huang, Yueh Min; Li, Qiong; Lu, Guangming

doi:10.1186/s13673-018-0156-3

Cited by 29 publications

(18 citation statements)

References 47 publications

(64 reference statements)

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“…Also, we try to use Soft-NMS (soft non-maximum suppression) to provide a dynamic regression to improve the detection accuracy of small target objects and dense objects [21]. In addition, the flexible use of multi-level convolution feature fusion [22], the addition of fine-grained feature classification [23], and a more comprehensive evaluation method for multi-object detection [24], all make multi-object detection become more efficient and accurate.…”

Section: Object Detectionmentioning

confidence: 99%

An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks

Cao

Chen

Gao

2020

Hum. Cent. Comput. Inf. Sci.

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IntroductionThe main purpose of object detection is to identify and locate one or more effective targets from still image or video data. It comprehensively includes a variety of important techniques, such as image processing, pattern recognition, artificial intelligence and machine learning. It has broad application prospects in such areas such as road traffic accident prevention [1], warnings of dangerous goods in factories, military restricted area monitoring and advanced human-computer interaction [2,3]. Since the application scenarios of multi-target detection in the real world are usually complex and variable, balancing the relationship between accuracy and computing costs is a difficult task.The object detection process is traditionally established by manually extracting feature models, where the common features are represented by HOG (histogram of oriented AbstractObject detection methods aim to identify all target objects in the target image and determine the categories and position information in order to achieve machine vision understanding. Numerous approaches have been proposed to solve this problem, mainly inspired by methods of computer vision and deep learning. However, existing approaches always perform poorly for the detection of small, dense objects, and even fail to detect objects with random geometric transformations. In this study, we compare and analyse mainstream object detection algorithms and propose a multi-scaled deformable convolutional object detection network to deal with the challenges faced by current methods. Our analysis demonstrates a strong performance on par, or even better, than state of the art methods. We use deep convolutional networks to obtain multi-scaled features, and add deformable convolutional structures to overcome geometric transformations. We then fuse the multi-scaled features by up sampling, in order to implement the final object recognition and region regress. Experiments prove that our suggested framework improves the accuracy of detecting small target objects with geometric deformation, showing significant improvements in the trade-off between accuracy and speed.

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Section: Object Detectionmentioning

confidence: 99%

An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks

Cao

Chen

Gao

2020

Hum. Cent. Comput. Inf. Sci.

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“…For CohnKanade( CK+) database, the accuracy recorded was 95.79%. Sun et al [14] have suggested a method to search three kinds of active regions, i.e., left eye regions, right eye regions, and mouth regions. CNN is used to extract information and classify expressions.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition System (FERS): A Survey

Mishra

Gupta²,

Mishra

2020

Smart Innovation, Systems and Technologies

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Human facial expressions and emotions are considered as the fastest way of the communication medium for expressing thoughts. The ability to identify the emotional states of people surrounding us is an essential component of natural communication. Facial expression and emotion detector can be used to know whether a person is sad, happy, angry, and so on. We can better understand the thoughts and ideas of a person. This paper briefly explores the idea of recognizing the computerized facial expression detection system. First, we have discussed an overview of the facial expression recognition system (FERS). Also, we have presented a glimpse of current technologies that are used for the detection of FERS. A comparative analysis of existing methodologies is also presented in this paper. It provides the basic information and general understanding of up-to-date state-of-the-art studies; also, experienced researchers can look productive directions for future work.

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“…e discriminative features are also selected from triangle and line features with the multiclass AdaBoost algorithm. Sun et al [7] proposed an effective method for the selection of optimized active face regions. ey used convolution neural network (CNN) to extract features from optimized active face regions.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

Mahmood

Hussain

Iqbal

et al. 2019

Mathematical Problems in Engineering

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Facial expression recognition plays an important role in communicating the emotions and intentions of human beings. Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise. In this paper, we present a new framework for effective facial expression recognition from real-time facial images. Unlike other methods which spend much time by dividing the image into blocks or whole face image, our method extracts the discriminative feature from salient face regions and then combine with texture and orientation features for better representation. Furthermore, we reduce the data dimension by selecting the highly discriminative features. The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise. To show the robustness of the proposed framework, we used three publicly available challenging datasets. The experimental results show that the performance of the proposed framework is better than existing techniques, which indicate the considerable potential of combining geometric features with appearance-based features.

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Facial expression recognition using optimized active regions

Cited by 29 publications

References 47 publications

An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks

An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks

Facial Expression Recognition System (FERS): A Survey

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

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