Rotation-reversal invariant HOG cascade for facial expression recognition

Chen, Jinhui; Takiguchi, Tetsuya; Ariki, Yasuo

doi:10.1007/s11760-017-1111-x

Cited by 19 publications

(9 citation statements)

References 28 publications

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“…Cruz et al [11] explore the temporal derivative and adjacent frames by using new framework known as temporal patterns of oriented edge magnitudes. e cases of out-of-plane head rotations are handled using rotationreversal invariant HOD, presented by Chen et al [12]. ey also developed the cascade learning model to boost the classification process.…”

Section: Related Workmentioning

confidence: 99%

“…However, the disgust expression obtained the smallest recognition accuracy of 31.7%. Table 5 illustrates the comparative assessment of the proposed method with the existing state-of-the-art [6,[10][11][12][13][14] methods. In literature, the FER system presented in [11] has achieved the highest recognition accuracy rate of 93.66% which works on the nonoverlapping patches.…”

Section: Experiments On MMI Ck+ and Sfew Databasementioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Experiments On MMI Ck+ and Sfew Databasementioning

confidence: 99%

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

Mahmood

Hussain

Iqbal

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Feature extraction algorithms in traditional methods include histogram of gradient (HOG) and local binary pattern (LBP), common classification algorithms comprise support vector machine (SVM), K-Nearest Neighbour (KNN) and naive bayes model (NBM) [2][3][4][5][6]. However, these traditional methods, which are susceptible to human disturbances, remain some problems such as the huge difference in the distribution of the source domain and the target domain.…”

Section: Introductionmentioning

confidence: 99%

Research on Facial Expression Recognition Based on Improved Inception Network

Yang

Gong

2022

J. Phys.: Conf. Ser.

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Facial expression recognition is one of the hot research fields in computer vision. Deep learning method is widely used due to its excellent performance in feature characterization. However, convolutional neural network, which is commonly used in the field of image classification, is prone to challenges such as too many parameters, difficulty in training and too many network layers. We proposed an improved inception network to solve the above. In this model, small-scale filter was used to replace large-scale one and multi-channel features were extracted. In addition, a smaller stochastic gradient value introduced in the ReLU makes the weights of some neurons be able to update. AM-softmax is adopted as the loss function with respect of the issue that the large intra-class distance about expression features. The recognition rates of the proposed model on CK+ and FER2013 are 98.32% and 71.12%, respectively. The experimental results verify the effectiveness of the proposed algorithm.

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“…The rapid development of computer technology makes it possible for the applications of the convolutional neural network (CNN) [ 8 , 9 , 10 , 11 ], which requires high computing power. Compared with traditional target detection algorithms like HOG-SVM (Histogram of Oriented Gradients-Support Vector Machine) [ 12 , 13 ], DPM (Deformable Parts Model) [ 14 , 15 ], and HOG-Cascade [ 16 , 17 ], CNN-based target detection algorithms have great advantages in many aspects such as speed and accuracy. Convolutional neural network (CNN) is a kind of feed forward neural network with convolutional computing and it usually has a deep structure.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection

2020

Sensors

100

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Remote sensing targets have different dimensions, and they have the characteristics of dense distribution and a complex background. This makes remote sensing target detection difficult. With the aim at detecting remote sensing targets at different scales, a new You Only Look Once (YOLO)-V3-based model was proposed. YOLO-V3 is a new version of YOLO. Aiming at the defect of poor performance of YOLO-V3 in detecting remote sensing targets, we adopted DenseNet (Densely Connected Network) to enhance feature extraction capability. Moreover, the detection scales were increased to four based on the original YOLO-V3. The experiment on RSOD (Remote Sensing Object Detection) dataset and UCS-AOD (Dataset of Object Detection in Aerial Images) dataset showed that our approach performed better than Faster-RCNN, SSD (Single Shot Multibox Detector), YOLO-V3, and YOLO-V3 tiny in terms of accuracy. Compared with original YOLO-V3, the mAP (mean Average Precision) of our approach increased from 77.10% to 88.73% in the RSOD dataset. In particular, the mAP of detecting targets like aircrafts, which are mainly made up of small targets increased by 12.12%. In addition, the detection speed was not significantly reduced. Generally speaking, our approach achieved higher accuracy and gave considerations to real-time performance simultaneously for remote sensing target detection.

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Rotation-reversal invariant HOG cascade for facial expression recognition

Cited by 19 publications

References 28 publications

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

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

Research on Facial Expression Recognition Based on Improved Inception Network

Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection

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