Individual-free representation-based classification for facial expression recognition

Sun, Zhe; Hu, Zhengping; Wang, Meng; Zhao, Shuhuan

doi:10.1007/s11760-016-0999-x

Cited by 13 publications

(8 citation statements)

References 26 publications

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“…This might be due to the fact that [14] used the ReLU activation function which has comparatively low recognition rate than Swish activation function. [22] achieved the lowest accuracy of 61.68%. Our method achieved the average accuracy gain of 8% over the comparative methods.…”

Section: Comparative Studymentioning

confidence: 99%

“…In the first stage, we compared the performance of our method on these contemporary methods [1], [13], [14], [16], [17], and [22] on the JAFFE dataset and results in terms of accuracy are provided in Table III. The proposed method provided superior detection accuracy over the comparative methods.…”

Section: Comparative Studymentioning

confidence: 99%

“…These results show the effectiveness of the proposed method for FER on the JAFFE dataset over the contemporary methods. [1] 92.8% Sun et al [22] 61.68% Khaliluzzamanet al [13] 93.02% Dubey et al [14] 93.75% Kola et al [17] 88.3% LBP+ORB [16] Jain et al [40] 92.4% 95.23% Proposed Model 95.02%…”

Section: Comparative Studymentioning

confidence: 99%

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Efficient-SwishNet Based System for Facial Emotion Recognition

et al. 2022

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Facial emotion recognition (FER) is an important research area in artificial intelligence and has many applications i.e., face authentication systems, e-learning, entertainment, deepfakes detection, etc. FER is still a challenging task due to more intra-class variations of emotions. Although existing deep learning methods have achieved good performance for FER. However, still there exists a need to develop efficient and effective FER systems robust to certain conditions i.e., variations in illumination, face angles, gender, race, background settings and people belonging to diverse geographical regions. Moreover, a generalized model for classification of human emotions is required to be implemented in computer systems so that they can interact with humans according to their emotions and improve their interaction. This work presents a novel light-weight Efficient-SwishNet model for emotion recognition that is robust towards the aforementioned conditions. We have introduced a low-cost, smooth unbounded above and bounded below Swish activation function in our model. Property of unboundedness helps to avoid saturation while smoothing helps in optimization and generalization of the model. Performance of the proposed model is evaluated on five diverse datasets including CK+, JAFFE, FER-2013, KDEF and FERG datasets. We also performed cross-corpora evaluation to show the generalizability of our model. The proposed model achieves very high recognition rate for all datasets that proves the merit of the proposed framework for both the human facial images and stylized cartoon characters. Moreover, we conducted an ablation study with different variants of our model to prove its efficiency and effectiveness for emotions identification.

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Section: Comparative Studymentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

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Efficient-SwishNet Based System for Facial Emotion Recognition

et al. 2022

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“…Zhao et al [73] proposed an instance-based transfer learning approach with multiple feature representations. Sun et al [51] proposed Individual Free Representation-Based Classification (IFRBC) that utilizes Variation Training Set (VTS) and virtual VTS for remitting the side effects caused by the individual differences. Wu et al [63] proposed Adaptive Feature Mapping (AFM) for transforming the feature distribution of testing samples into that of training samples.…”

Section: Related Workmentioning

confidence: 99%

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Kartheek

Prasad

Bhukya

2021

Complex Intell. Syst.

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Facial expressions are predominantly important in the social interaction as they convey the personal emotions of an individual. The main task in Facial Expression Recognition (FER) systems is to develop feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting distinctive features, Radial Cross Pattern (RCP), Chess Symmetric Pattern (CSP) and Radial Cross Symmetric Pattern (RCSP) feature descriptors have been proposed and are implemented in a 5 $$\times $$ × 5 overlapping neighborhood to overcome some of the limitations of the existing methods such as Chess Pattern (CP), Local Gradient Coding (LGC) and its variants. In a 5 $$\times $$ × 5 neighborhood, the 24 pixels surrounding the center pixel are arranged into two groups, namely Radial Cross Pattern (RCP), which extracts two feature values by comparing 16 pixels with the center pixel and Chess Symmetric Pattern (CSP) extracts one feature value from the remaining 8 pixels. The experiments are conducted using RCP and CSP independently and also with their fusion RCSP using different weights, on a variety of facial expression datasets to demonstrate the efficiency of the proposed methods. The results obtained from the experimental analysis demonstrate the efficiency of the proposed methods.

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“…Ali et al (2017) proposed a method for FER based on Histogram of Oriented Gradients (HOG) characteristics and applied it on KDEF and RaFD datasets. Sun et al (2017) proposed Individual Free Representation Based Classification (IFRBC) for remitting the side effects caused by individual differences in both training and query images simultaneously. Turan et al (2018) proposed Soft Locality Preserving Map (SLPM), a graph based manifold learning method for expression recognition.…”

Section: Related Workmentioning

confidence: 99%

Radial mesh pattern: a handcrafted feature descriptor for facial expression recognition

Kartheek

Prasad

Bhukya

2021

J Ambient Intell Human Comput

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Facial expressions are extremely important in the social interaction as they can display the internal emotions and intentions of an individual. Accurately classifying the facial expressions into various categories is the main task in Automatic Facial Expression Recognition (AFER) systems. The existing local based techniques, at times suffer and generate same feature values for different image portions such as edge, corner and flat regions. To address this issue, Radial Mesh Pattern (RMP), a local texture based approach based on the chess game rules is proposed. With reference to the center pixel in a 5 × 5 neighborhood, the possible positions of Rook, Bishop and Knight are determined and based on these positions, the features are extracted. In this paper, not only binary weights, but also other weights such as fibonacci, prime, natural, squares, odd and even weights have been utilized for feature extraction. To validate the efficiency of the proposed method, RMP is implemented on six 'in the lab' datasets. The performance is measured through recognition accuracy and the results obtained from experiments demonstrate the efficiency of RMP over standard existing methods.

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Individual-free representation-based classification for facial expression recognition

Cited by 13 publications

References 26 publications

Efficient-SwishNet Based System for Facial Emotion Recognition

Efficient-SwishNet Based System for Facial Emotion Recognition

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Radial mesh pattern: a handcrafted feature descriptor for facial expression recognition

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